KServe Company Research Skill

This skill produces a comprehensive BD intelligence report on a prospect company so KServe's business development team can reach out to the right people with the right message. The output is a structured chat summary with verified sources for every data point.

Compatible with: Claude.ai · Claude Code · Cowork · OpenCode · Codex · Any AI agent platform

About KServe

KServe is an AI-powered Business Process Outsourcing (BPO) company headquartered in Thane, Maharashtra, India. KServe helps businesses grow and operate more efficiently by taking over key business functions — powered by integrated AI technology that delivers faster turnaround, higher accuracy, and better outcomes than traditional BPO.

Services

All services can be augmented with KServe's AI technology — enabling automation, smarter routing, predictive insights, and higher throughput at lower cost.

Target Industries

BFSI · NBFC · Banking & Securities · Insurance · eCommerce · Education / EdTech · Automobile · Energy & Utilities · Healthcare · Media & Entertainment · Real Estate · Retail · Manufacturing · Tours & Travel · Hospitality · Agriculture · Immigration · Accounting · Fintech · Food & Beverages · Supply Chain Management · Logistics

Platform Execution Mode

Detect your execution mode before starting. Apply it consistently throughout.

If unsure, default to SEQUENTIAL — it is always safe, just slower.

PARALLEL: Spawn in two waves after user confirms.

Wave 1 — spawn simultaneously: Workers 2, 3, 4, 5, 6, 6B, 7, 7B, 7C, 8, 9, 11, 12, 13, 14. Each Worker runs its own Checker loop independently.

Wave 2 — spawn only after ALL Wave 1 workers have been Checker-approved: Workers 10 (KServe Fit) and 10B (ICP Score). Steps 10 and 10B read the approved outputs from Steps 2–9 before executing. Do not spawn Workers 10 or 10B until Wave 1 is fully complete.

Orchestrator assembles the final report once Workers 10, 10B, and all Wave 1 workers are complete.

PARALLEL — Progress reporting: After spawning Wave 1, immediately post a status board to the user:

Research started for [Company Name]. Running 15 parallel workers:
⏳ In progress: Steps 2, 3, 4, 5, 6, 6B, 7, 7B, 7C, 8, 9, 11, 12, 13, 14
⏸️  Waiting to spawn: Steps 10 & 10B (KServe Fit + ICP Score — start after Wave 1 completes)
I'll update you as sections complete.

As each Worker is approved by its Checker, post a one-line update: ✓ [Step name] complete — [1-phrase summary, e.g., "Turnover: ₹847 Cr FY24"]

When Wave 1 is fully complete: Wave 1 complete. Spawning KServe Fit (Step 10) and ICP Score (Step 10B). Assembling final report…

SEQUENTIAL: Run Steps 2–15 in order. Complete each Worker → Checker loop before advancing. After each step is approved, immediately append the completed section to the output with prefix ✓ [Step name] complete: — do not buffer until Step 15. Exception: Step 10 (KServe Fit) cannot stream early — it depends on Steps 2–9 all being approved first, but in SEQUENTIAL mode this is naturally guaranteed. After Step 15, append the BD Briefing and DATA QUALITY footer to complete the report.

Tool naming across platforms:

• Web search: web_search, WebSearch, search, browse, or equivalent
• File write: write_file, Write, fs.write, or equivalent
• Subagents: Task, spawn_agent, create_agent, or platform equivalent

Research Flow

Phase 1 — Verification (always first, on every platform)

Search the web for the company. Present the user with:

• Company name (as found)
• Website URL
• Registered / primary address
• Brief one-line description

Stop and wait for the user to confirm this is the right company before proceeding. If the user provided a website or address, use it to narrow the search.

Example:

I found the following. Is this the company you mean?

**Name:** Reliance Retail Ltd.
**Website:** https://www.relianceretail.com
**Address:** 3rd Floor, Court House, Lokmanya Bal Gangadhar Tilak Marg, Mumbai – 400002
**About:** India's largest retail chain across grocery, fashion, and electronics.

Please confirm and I'll run the full research.

Phase 2 — Full Research (after user confirms)

Run all 14 research steps (Steps 2–15) using the execution mode detected above. Each step follows the Worker → Checker → Orchestrator pattern:

1. Worker gathers data for that step using available web/search tools
2. Checker validates the output against the seven criteria (see Checker Instructions)
3. If anything fails, Checker returns specific feedback to Worker — loop repeats
4. Once approved, output passes to the Orchestrator
5. Orchestrator assembles the final report once all steps are complete

Core Research Principles

These principles apply to every step and every platform. Read them before executing any step.

Recency first. Prioritize sources from the last 12 months. If only older data is available, use it but note in report: ⚠️ Most recent available: [FY/date]. Newer data may not yet be public.

Every data point needs a source. Never present a fact without a URL or document reference. If something cannot be sourced, write "Not publicly available" — do not guess.

MCA is ground truth for Indian companies. For these fields, always use MCA (mca.gov.in) as the primary source:

• Incorporation date (Step 5)
• Current directors (Step 6)
• Registered address (Step 4)
• Financial filings / turnover (Step 3)

Tofler, Zauba Corp, and similar aggregators pull from MCA and are acceptable secondary sources.

BD framing throughout. Every section must be written with the lens of: "How does this help KServe win this account?" — not raw data, but insight.

Graceful degradation. If a tool or data source is unavailable, note it clearly in that section and move on. Never halt the entire report because one step hit a wall.

Rate-limited or gated sources. If a source returns a 429, access-denied, or login-required response:

1. Do not retry the same source more than once immediately.
2. Switch to the next source in the Source Priority table for that step.
3. Note in the report: ⚠️ [Source name] access denied/rate-limited — [fallback source] used instead.
4. If ALL sources for a step are gated: flag the step as RETRY_EXHAUSTED with reason "all sources gated" and move on without loop-retrying.

Commonly gated sources — handle proactively:

• Tracxn: Check the public company URL first (often accessible); only flag gated if you hit a login wall on the detail page.
• LinkedIn: Company page follower count and basic info are publicly visible without login. Individual profiles may be limited. Job posting counts on LinkedIn Jobs are public without login.
• MCA AOC-4 filings: Full Annual Return sometimes requires Tofler or Zauba Corp as proxy — this is expected behavior, not a failure.

Zero results = explicit statement. If a web search returns no results for a required field, write "Not publicly available" or "No results found" in the report. Never fill a gap by inferring from adjacent context, similar companies, or general knowledge. An acknowledged gap is always more trustworthy than an unverified inference — and an incorrect data point handed to BD is actively harmful.

Source Priority Reference

Use this table for every step. Each step lists which sources to try in order of preference.

Research Steps (Steps 2–15)

Step 2 — Line of Business

Find: industry, core products/services, business model (B2B / B2C / B2G), key customer segments.

Step 3 — Turnover (₹ Crores)

Find annual revenue/turnover in Indian Rupees (Crores). Always include the financial year (e.g., FY2023-24).

For Indian-registered companies: use MCA annual filings → Tofler/Zauba → news. For non-Indian companies or Indian subsidiaries of foreign entities: report in original currency, convert to INR at filing-date exchange rate, and note in report: Revenue in [currency]; converted to INR at [rate] as of [date]. If not publicly available: write "Private company — turnover not publicly disclosed."

Step 4 — Head Office Location

Find the primary registered office address. Cross-reference MCA registered address against company website — they sometimes differ. If different, report both: Registered (MCA): [address] | Current operations (website): [address]

Step 5 — Years in Existence

Find the incorporation / founding year. Calculate age from today.

Step 6 — Directors

Pull current directors from MCA. For each: Full name · Designation (MD, Director, Independent Director, etc.) · DIN (Director Identification Number).

For BD outreach, flag directors likely to be decision-makers for outsourcing: MD, COO, CFO, VP Operations. Mark each with a star (★) to distinguish from board/independent directors.

LinkedIn lookup (for ★-flagged directors only): Search "[Full name]" "[Company name]" LinkedIn for each BD-relevant director. Record:

• LinkedIn profile URL (if publicly accessible)
• Tenure at this company (from LinkedIn experience section)
• Last post or activity date (if public)
• Flag ★ NEW if appointed/joined within the last 6 months — a new MD, COO, or CFO is a high-value trigger (new leadership re-evaluates vendor relationships)

If LinkedIn is not accessible for a director: write LinkedIn: Not publicly accessible.

Output format for directors: ★ [Name] — [Designation] — DIN: [XXXXXXXX] — LinkedIn: [URL or "Not accessible"] — Tenure: [X years / ★ NEW (<6 months)] [Name] — [Designation] — DIN: [XXXXXXXX] (for non-BD-relevant directors, no LinkedIn lookup needed)

Step 6B — Decision-Maker Dossiers

For each ★-flagged BD-relevant director whose LinkedIn profile is publicly accessible, produce a 3-line brief:

• Line 1 — Background: Previous 2–3 companies and roles (from LinkedIn experience). Industry experience duration. Any notable career inflection (e.g., "former McKinsey Principal — transitioned to operational roles in BFSI").
• Line 2 — LinkedIn activity: Last post or like date (if visible). Content themes of recent public posts (e.g., "posts about operational scaling and team culture"). If no public activity: No public LinkedIn activity visible.
• Line 3 — Likely first objection: Based on their background, what is the most probable pushback to a KServe pitch? (e.g., ex-McKinsey CFO: "will demand ROI data upfront and cost-per-unit comparison"; founder-CEO of bootstrapped startup: "trust and control concerns — prefers to start with a pilot"; career ops leader: "will ask about SLA guarantees and transition risk")

If LinkedIn is not publicly accessible for a director: produce Line 1 only from public sources (company website bio, news articles, conference speaking history). Mark Lines 2–3 as Not accessible.

Do not speculate on personal information beyond professional public record.

Step 7 — Branches & Offices

Find: total number of offices/branches/locations · key cities/states · any international presence.

Step 7B — Job Postings (Outsourcing Intent Signals)

Search for active job postings to reveal what functions the company is actively trying to fill — a direct signal of where they have resource gaps KServe can address.

Sources (try in order):

1. site:naukri.com "[company name]" — primary for Indian companies
2. LinkedIn Jobs: "[company name]" filter by India
3. Indeed India · Company careers page

Find:

• Approximate number of open roles (not exact — a range is fine, e.g., "15–20 open roles")
• Top 3 functions with the most open roles (e.g., "Customer Support: 12, Back-Office: 5, Collections: 4")
• Keywords in JDs that signal outsourcing pain: "manage high volume," "handle escalations," "coordinate with outsourcing vendor," "work with BPO partner," "process-driven," "high-throughput"
• Roles that directly match KServe's services: Customer Service agents, Collections executives, Lead Generation reps, Data Entry / Back-Office Processing staff, Market Research analysts

BD framing: List 2–3 open roles most directly relevant to KServe's services: Format: [Role title] — [KServe service match] — [approximate count or "multiple"] Then: 1-sentence BD signal — what does this hiring pattern imply about the company's current resourcing pressure?

If no public job postings found: Write No active job postings found on Naukri, LinkedIn Jobs, or Indeed India as of [date]. Company may not be publicly recruiting, or postings may be behind a login wall.

Source(s): [URLs] | Confidence: HIGH/MED/LOW | Checked: YYYY-MM-DD

Step 7C — Technology Stack

Search for what technology tools the company uses — reveals digital maturity, existing vendor relationships, and integration opportunities for KServe.

Sources (try in order):

1. BuiltWith (builtwith.com) — enter company domain
2. Wappalyzer (wappalyzer.com) — enter company domain
3. Job postings: look for technology mentions in JD requirements (e.g., "experience with Salesforce CRM," "proficient in Zendesk")
4. Company website footer: check for cookie/analytics vendor tags, embedded chat widget vendor logos

Find:

• CRM in use (Salesforce, HubSpot, Zoho, LeadSquared, etc.) — KServe can operate within these
• Customer support tool (Zendesk, Freshdesk, Intercom, etc.) — KServe's CS team plugs in without migration
• Review management tool (cross-reference Step 8E findings)
• Marketing automation platform (signals marketing team maturity and data readiness)

BD framing: For each tool found: [Tool] detected — KServe integrates natively with this stack; no migration required.

If no tech stack data accessible: Write Tech stack not publicly detectable via BuiltWith, Wappalyzer, or job postings as of [date].

Source(s): [URLs] | Confidence: HIGH/MED/LOW | Checked: YYYY-MM-DD

Step 8 — Reviews & Reputation (Last 12 Months)

Research all five sub-sections below. Each sub-section is mandatory — if no data is found for a sub-section, state that explicitly (e.g., "No product reviews found on searched platforms"). Do not silently omit any sub-section.

If less than 12 months of reviews exist for any sub-section, include older reviews and note: ⚠️ Full 12-month data unavailable; includes reviews from [date range].

A — Product Review

Search for product-specific reviews and, if the company has a consumer app, app store reviews.

Product review sources (use platforms relevant to the company type):

App review sources:

1. Primary — direct search: site:apps.apple.com [company name] and site:play.google.com [company name]
2. Fallback — AppFollow.io, AppBot.co (often accessible without paywall)
3. Last resort — Reddit, Product Hunt, tech blogs

Find:

• Platform, aggregate rating, review volume
• Top recurring themes from critical reviews (1–2 stars): note the most frequent complaints, platforms, and date range
• Top recurring themes from positive reviews (4–5 stars): same format
• For apps: app name, platform (iOS / Android / Both), aggregate rating; any visible company replies on reviews — note whether they appear on positive, negative, or both review types; include one verbatim example ≤40 words
• If no app found: write No consumer app found — app review not applicable and document the search query used
• If app found but reviews are gated: write App found ([name] — [rating]/5 on [platform]) but individual review content is gated

Sentiment trend (12-month window): After gathering themes, assess directional trend by sorting visible reviews from oldest to newest. Classify as:

• Improving — negative themes decreasing, positive themes increasing over the window
• Worsening — opposite pattern
• Stable — no clear directional shift
• Insufficient data — fewer than 10 reviews in the 12-month window

Include in output: Trend: [Improving / Worsening / Stable / Insufficient data] — [1-sentence observation, e.g., "Complaints about app crashes spiked in Q3 2025 and have partially subsided in Q4"]

BD framing: what does product/app quality feedback signal about operational gaps KServe can address?

B — Service Review

Search for customer sentiment about how the company serves its customers — support quality, delivery, responsiveness, experience.

Sources:

• Google Business Profile, Trustpilot, Justdial (all company types)
• Finance / Insurance: banking ombudsman forums, consumer court portals, RBI complaint trackers
• B2B: IndiaMart seller ratings, LinkedIn client testimonials, absence of case studies as a signal

Find:

• Recurring themes in customer service feedback: response time, resolution quality, staff attitude
• Specific complaints about support channels (phone, chat, email, in-store)
• Platforms checked with review count and date range

Sentiment trend (12-month window): Same method as Section A — classify as Improving / Worsening / Stable / Insufficient data, with a 1-sentence observation.

BD framing: where does customer service break down? This maps directly to KServe's Customer Service offering.

C — Employee Review

Search for what employees say about working at this company — signals operational maturity, management culture, and attrition risk.

Sources:

• AmbitionBox (primary for Indian companies), Glassdoor, Indeed
• LinkedIn "Reviews" tab if available

Find:

• Overall rating on each platform, review volume
• Top 3 positives (recurring themes)
• Top 3 negatives (recurring themes)
• Any mentions of process quality, training programs, tech stack, attrition rate, management style

BD framing: high attrition or poor internal process = outsourcing appetite; strong culture = partnership-friendly decision-maker.

D — General Company Review

Search for overall public perception, business reputation, and press sentiment — the narrative that doesn't fit product or service reviews.

Sources:

• Google Business (overall company rating, not product/service-specific)
• Industry forums and discussion boards
• News and media sentiment (last 12 months)
• Justdial, IndiaMart general business listings

Find:

• Overall public reputation narrative
• Any controversies, regulatory issues, or legal disputes
• Any awards, recognitions, or positive press
• Key sentiment trend (improving / stable / declining)

BD framing: reputational context that shapes outreach tone — a company under scrutiny needs a different pitch than one riding a growth wave.

E — Review Handling Methodology

Research how the company manages and responds to reviews across all platforms found in A–D. Use what was already observed in A–D as your primary signal. Run targeted additional searches only for tool detection (job postings, BuiltWith) — do not re-research review content already gathered.

Look for evidence of:

Synthesize into:

• Response channel(s) used
• Response style: Personalized / Templated / Mixed / Not found
• Response rate estimate with basis
• Response speed estimate
• Tool indicators (any third-party review management tools detected)
• BD signal: what does this methodology reveal about the company's CS culture, and where does KServe's offering address the gap? Must be an implication, not a description ("They respond to reviews" is not BD insight).

If no responses found across any platform: No review responses found across [list platforms checked] — no active review management program detected, or handling is off-platform (email/DM only).

Step 9 — Overall Business Rating (out of 10)

Assign a synthesized reputation score — NOT an average of star ratings. Base it on the review themes from Step 8.

Rating anchors:

Provide a 2–3 sentence rationale. Note: a lower score often signals more BPO opportunity for KServe.

Sample size caveat: If fewer than 15 total reviews were found across all Step 8 platforms combined, note in the rationale: ⚠️ Low sample: score based on [N] total reviews across [platforms] — Confidence: LOW. Treat as directional only. If zero reviews were found across all platforms: write Rating: N/A — insufficient review data. Confidence: LOW.

Step 10 — KServe Services Fit

Depends on: Steps 2–9 (LoB, size, reviews, directors). In PARALLEL mode, run this step last — after all other workers complete.

Based on the full research picture, recommend 3–5 services (not all 8) with explicit fit levels:

• ⭐ HIGH FIT — service directly addresses a visible pain point found in reviews or news
• ✅ MEDIUM FIT — service aligns with company strategy, size, or industry norms

Decision rules:

• HIGH FIT requires at least ONE of: (a) explicit pain-point evidence in Step 8 reviews/news, (b) open job requisitions in that function found in research, (c) a specific recent event (funding, expansion, leadership change) that makes the service directly timely.
• MEDIUM FIT requires at least ONE of: (a) industry norm (e.g., NBFCs typically need Collection services), (b) company size signals that make the service plausible, (c) absence of an obvious in-house function (e.g., no published customer care number signals underdeveloped CS).
• Exclude a service entirely (do not list it) if the company's size or business model makes it implausible (e.g., a 10-person bootstrapped startup does not need Collection services; a pure B2G company rarely needs Lead Generation).
• At the end of the KServe Fit section, add one line: Excluded: [Service] — [reason] · [Service] — [reason] (only for excluded services, not all 8).

Format each as: [Service] — [Fit level] — [Specific evidence from research]

Example:

Customer Service ⭐ HIGH FIT
Glassdoor reviews cite "2-hour wait times" and "unresponsive support" — a direct signal
that in-house customer ops are stretched. KServe's AI-powered CX management addresses this.

Lead Generation ✅ MEDIUM FIT
Company is expanding into 3 new cities (per recent news). Qualified outbound lead gen
could accelerate market entry without growing headcount.

Step 10B — ICP Score (Ideal Customer Profile Score)

Depends on: Steps 2–10 (all prior research). In PARALLEL mode, run this step alongside Worker 10 (Wave 2), but only after Wave 1 is complete. In SEQUENTIAL mode, run after Step 10 is approved.

Do not run new web searches. Use only data from prior approved steps.

Compute a scored Ideal Customer Profile rating (0–100) for this company as a KServe prospect. Score each dimension:

Total: 100 points

Tier thresholds:

• 75–100 — Priority Tier 1: Assign senior AE; outreach within 48 hours
• 50–74 — Tier 2: SDR outreach; standard sequence
• 25–49 — Tier 3: Nurture list; revisit in 60 days
• 0–24 — Deprioritize: Flag to BD manager with rationale; do not assign AE

Format output as:

📈 ICP SCORE: [XX/100] — [Tier 1 / Tier 2 / Tier 3 / Deprioritize]
Score breakdown:
  Industry match: [X/15] — [reason]
  Revenue band: [X/10] — [reason]
  Employee proxy: [X/10] — [reason]
  Pain point evidence: [X/15] — [reason]
  Review quality: [X/10] — [reason]
  Growth/change: [X/10] — [reason]
  Decision-maker access: [X/10] — [reason]
  Job postings: [X/10] — [reason]
  Social presence: [X/5] — [reason]
  Data confidence: [X/5] — [reason]
Primary drivers: [top 2 dimensions that most influenced the score]
Recommended action: [48h AE outreach / SDR sequence / Nurture / Deprioritize]

Step 11 — Customer Care Number

Find their publicly listed customer support / helpline number.

BD insight: presence of a published number signals a formal support structure. Absence may indicate underdeveloped customer ops — a potential KServe entry point. If no number is found, note in report: "No published support number found."

Step 12 — Social Media Followers

Pull current follower counts: LinkedIn · Instagram · Facebook · Twitter/X · YouTube (if applicable).

Engagement signal (check the main platform — LinkedIn for B2B, Instagram for B2C):

• Review last 5–10 posts on the primary platform.
• Calculate observed engagement rate: (total likes + comments on sampled posts) ÷ (posts sampled × follower count).
• Apply platform-calibrated thresholds:

• Flag low engagement as: Low engagement — [platform]: [engagement rate]% (based on [N] posts sampled)
• Flag if posting frequency is < 2×/month across all active platforms.
• If follower count is below 1,000 on all platforms: write Low follower base (< 1,000 on all platforms) — engagement rate not meaningful; flag as early-stage social presence.
• Note sample size: Engagement rate calculated on [N] posts as of [date]

Step 13 — Tracxn Profile

Search Tracxn.com for the company. Report: Tracxn Score (0–100 scale, if available) · category/sector tags · funding stage · investors · notable badges.

If company is not on Tracxn (common for traditional/non-VC companies): note in report Not on Tracxn — likely private/bootstrapped. and check Crunchbase as fallback.

If Tracxn profile requires a paid subscription to view detail: note in report Tracxn profile exists but detail is gated.

Crunchbase depth (use as secondary source or Tracxn fallback):

• Employee count range (Crunchbase shows this for most companies, even private): e.g., 51–200 employees
• Funding timeline: list each round with date, amount, and lead investor
• Total funding raised to date
• Investor tier: classify lead investor as Tier 1 (Sequoia, Accel, Lightspeed, SoftBank, Tiger Global), Tier 2 (regional/corporate VC, family office), or Bootstrapped/Angel

BD signal from funding profile:

• Tier 1 backed = high growth pressure, active scaling, likely receptive to outsourcing
• Recent Series B/C without profitability signal = cost-consciousness may push back on new spend; lead with ROI
• Bootstrapped = founder is the decision-maker, single-call close possible; trust-building is priority
• Late-stage PE fund (vintage >5 years) = exit pressure, cost optimization is top of mind

Step 14 — Acquisitions & M&A Activity

Search for any recent (last 12 months preferred): acquisitions · being acquired · mergers · major investment rounds · PE/VC backing changes.

Additional searches:

• Government contracts: Search gem.gov.in "[company name]" — note if they are an active GeM (Government e-Marketplace) supplier. This signals compliance maturity, longer procurement timelines, and that the company operates in regulated environments. BD implication: open with compliance and documentation-quality credentials.
• PE ownership depth: If Tracxn or Crunchbase shows PE backing, search for the PE firm's portfolio page. Note: PE firm name · stake held (majority/minority/not specified) · fund vintage year. Fund vintage BD signal: A PE fund 6+ years into a typical 10-year cycle is approaching exit horizon — cost reduction programs are usually underway; outsourcing is a direct lever.

BD signals:

• Being acquired → may freeze vendor decisions (note in report)
• Fresh funding raised → likely expanding, open to outsourcing (highlight as trigger signal for Step 15)
• GeM supplier → compliance-driven, longer sales cycle, pitch with documentation accuracy and audit trails
• Late-stage PE backing → cost reduction is a stated goal; lead with cost-per-transaction vs. in-house comparison

Step 15 — BD Intelligence Briefing

Most important step. Synthesize findings from Steps 2–14 into actionable outreach intel. Do not run new web searches — use only what was gathered in prior steps.

QUALITY GATE — Before synthesizing, the Step 15 Worker must:

1. Count RETRY_EXHAUSTED signals from prior steps. If 4 or more steps exhausted retries, open the BD Briefing section with: ⚠️ Partial data warning: [N] research steps returned best-available data only. The briefing below reflects current research confidence — validate key points before outreach.
2. If Step 10 (KServe Fit) is RETRY_EXHAUSTED or missing, omit Section C (Trigger Signals) entirely. Replace with: Trigger signals omitted — KServe Fit data unavailable. Re-run Step 10 before outreach.
3. If Step 8 (Reviews) is RETRY_EXHAUSTED, omit review-based Conversation Starters from Section B. Use funding, expansion, or leadership hooks from Steps 14/6 only.

A. Things to Know Before Reaching Out (3–5 bullet points) Current strategic focus · key decision-makers · recent challenges visible in research.

If Step 8E found response rate Low or None: include a bullet noting the visible CS gap — list platforms checked and the estimated response rate. If Step 8A found an app rating below 3.5/5 with meaningful review volume (typically 50+ ratings, or fewer if the platform prominently displays them): include a bullet on the app reputation risk and whether the company is actively engaging with it.

B. Conversation Starters (3–5 specific, recent hooks) Based on actual events found in research (expansion, funding, product launch, leadership hire, negative reviews). Format: "[Company] recently [event] — we've helped similar companies with [KServe service] in situations like this."

If Step 8E found templated or absent review responses, use: "[Company] has [X] total reviews on [platform] with a [High/Medium/Low/None] reply rate — we've helped similar [industry] companies set up structured review response programs as part of a broader CX operation." Use only if evidenced in Step 8E — do not fabricate reply-rate label or platform details.

C. Trigger Signals — Why Reach Out Now (top 2–3 only) Select the most compelling from:

• Rapid hiring (scaling pain) · Geographic expansion · New product/service launch
• Negative reviews spiking · Funding round closed · Leadership change
• App store rating below 3.5/5 with high review volume → visible product/service quality signal
• Review response rate Low or None across multiple platforms → underdeveloped CS infrastructure
• Third-party review tool detected (e.g., Birdeye, Yotpo) → pitch shifts from "you need this" to "we can operate this for you at scale"

D. Potential Objections & Responses (2–3 only) Based on company profile, anticipate likely pushbacks and provide a suggested KServe response for each.

If a review management tool was detected in Step 8E, anticipate: "We already use [tool] to manage reviews." Suggested response: "That's exactly the setup we integrate with — KServe handles the human judgment layer (response drafting, escalation routing) within your existing tool. You keep the tech stack, we remove the headcount burden."

E. Next Best Action (exactly one recommendation)

Synthesize all research into a single, specific, ranked action for the BD rep. This is a decision, not a summary.

Format: [Do X] — [because Y] — [contact: {named director from Step 6}] — [via: {LinkedIn InMail / phone / email}] — [hook: {specific finding from research}]

Example: "Call Priya Mehta (CFO, LinkedIn: accessible) within 48 hours — 3 open Collections executive roles on Naukri signal active scaling pressure; lead with: 'We've helped 4 NBFCs build their collections function in 90 days without the compliance risk of in-house hiring.'"

Decision rules:

• The action must reference at least ONE specific finding from research (not a generic claim)
• The contact must be a named director from Step 6 with LinkedIn accessible — not a generic "operations head"
• The outreach channel must be specific (LinkedIn InMail / phone / email — not "reach out")
• If ICP Score (Step 10B) is Tier 3 or Deprioritize: action = Add to [60-day / 90-day] nurture sequence — do not assign AE yet. Monitor for: [specific trigger to watch, e.g., next funding round announcement, next Glassdoor spike]

Output Format

Present the final report using this template:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🏢 KSERVE BD RESEARCH REPORT
Company: [Name]
Research Date: [Date]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✅ VERIFICATION
[Website | Address | Confirmed by user]

📋 LINE OF BUSINESS
[Summary]
Source(s): [URL] | [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

💰 TURNOVER
[₹ X Crores | FY XXXX-XX]
Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

📍 HEAD OFFICE
[Address]
Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

📅 YEARS IN EXISTENCE
[Founded XXXX | X years old]
Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

👔 DIRECTORS
★ [Name] — [Designation] — DIN: [XXXXXXXX] — LinkedIn: [URL or "Not accessible"] — Tenure: [X years / ★ NEW (<6 months)]
[Name] — [Designation] — DIN: [XXXXXXXX]
Source(s): [MCA URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

🧑‍💼 DECISION-MAKER DOSSIERS
★ [Name] — [Designation]
  Background: [previous companies / roles / industry tenure]
  LinkedIn activity: [last post date + content themes / "No public activity visible"]
  Likely first objection: [specific pushback based on background]
[Repeat for each ★-flagged director with accessible LinkedIn]
Source(s): [LinkedIn URLs] | Confidence: HIGH/MED/LOW | Checked: YYYY-MM-DD

🗺️ BRANCHES & OFFICES
[X locations | Key cities]
Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

💼 JOB POSTINGS (Intent Signals)
Open roles: ~[N] | Top functions: [e.g., Customer Support: 12, Back-Office: 5, Collections: 4]
KServe-relevant openings:
  [Role title] — [KServe service match] — [count]
BD signal: [what this hiring pattern implies about resourcing pressure]
Source(s): [URLs] | Confidence: HIGH/MED/LOW | Checked: YYYY-MM-DD

🛠️ TECHNOLOGY STACK
CRM: [Tool / Not detected] | Support tool: [Tool / Not detected] | Review tool: [Tool / Not detected (see 8E)]
Marketing automation: [Tool / Not detected]
BD framing: [integration angle]
Source(s): [URLs] | Confidence: HIGH/MED/LOW | Checked: YYYY-MM-DD

⭐ REVIEWS & REPUTATION (Last 12 months)

📦 A — PRODUCT REVIEW
[If no product reviews found: "No product reviews found on searched platforms"]
[Repeat for each platform checked:] Platform — Rating — Review count — URL
Top critical themes: ...
Top positive themes: ...
Trend: [Improving / Worsening / Stable / Insufficient data (<10 reviews)] — [1-sentence observation]
App:
  [Found]: [name] | [iOS / Android / Both] | [X.X]/5 ([X,XXX] ratings)
    Sample critical: "[verbatim ≤40 words]" — [reviewer] — [YYYY-MM-DD] | [URL]
    Sample positive: "[verbatim ≤40 words]" — [reviewer] — [YYYY-MM-DD] | [URL]
    Company replies on app: Yes (on: positive / negative / both) / No / Partial
      [If Yes or Partial:] Sample company reply: "[verbatim ≤40 words]" — [YYYY-MM-DD] | [URL]
  [Gated]: "App found ([name] — [rating]/5 on [platform]) but individual review content is gated. Search query used: [query]"
  [Not found]: "No consumer app found — app review not applicable. Search query used: [query]"
Confidence: HIGH/MED/LOW | Most recent: YYYY-MM-DD

🛎️ B — SERVICE REVIEW
[If no service reviews found: "No service reviews found on searched platforms"]
[Repeat for each platform checked:] Platform — Rating — Review count — URL
Top positives: ...
Top negatives: ...
Trend: [Improving / Worsening / Stable / Insufficient data] — [1-sentence observation]
Notable signal: [e.g., "No case studies found — signals limited B2B social proof" / "N/A"]
BD signal: [what this means for KServe's Customer Service pitch]
Confidence: HIGH/MED/LOW | Most recent: YYYY-MM-DD

👥 C — EMPLOYEE REVIEW
[If no employee reviews found: "No employee reviews found on searched platforms"]
[Repeat for each platform checked:] Platform — Rating — Review count — URL
Top positives: ...
Top negatives: ...
BD signal: [attrition / culture signal for outreach approach]
Confidence: HIGH/MED/LOW | Most recent: YYYY-MM-DD

🌐 D — GENERAL COMPANY REVIEW
[If no general reviews found: "No general company reviews found on searched platforms"]
[Repeat for each platform checked:] Platform — Rating — URL
Overall reputation narrative: ...
Notable: [awards / controversies / press sentiment]
BD signal: [outreach tone implication — e.g., "regulatory scrutiny: open with credibility" / "growth wave: lead with scale support"]
Confidence: HIGH/MED/LOW | Most recent: YYYY-MM-DD

🔄 E — REVIEW HANDLING METHODOLOGY
Response channel(s): [e.g., Google Maps native · App Store native / Not found]
Response style: Personalized / Templated / Mixed / Not found
Response rate: High (>60%) / Medium (20–60%) / Low (<20%) / None [sample: X reviews examined]
Response speed: <24h / 1–7 days / >7 days / Inconsistent / Not determinable
Tool indicators: [e.g., "Job posting cites Birdeye" / "None detected"]
BD signal: [1–2 sentences: CS culture implication and KServe opportunity]
Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

🎯 OVERALL RATING: X/10 — [Label]
[2–3 sentence rationale]

🤝 KSERVE FIT ASSESSMENT
[Service — Fit level — Evidence]
Excluded: [Service — reason] · [Service — reason]

📈 ICP SCORE: [XX/100] — [Tier 1 / Tier 2 / Tier 3 / Deprioritize]
Score breakdown:
  Industry match: [X/15] · Revenue band: [X/10] · Employee proxy: [X/10]
  Pain point evidence: [X/15] · Review quality: [X/10] · Growth/change: [X/10]
  Decision-maker access: [X/10] · Job postings: [X/10] · Social presence: [X/5] · Data confidence: [X/5]
Primary drivers: [top 2 dimensions]
Recommended action: [48h AE outreach / SDR sequence / Nurture / Deprioritize]

📞 CUSTOMER CARE NUMBER
[Number or "Not published"] | Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

📱 SOCIAL MEDIA FOLLOWERS
LinkedIn: X | Instagram: X | Facebook: X | Twitter/X: X | YouTube: X
Source(s): [URLs] | Confidence: HIGH/MED/LOW | Checked: YYYY-MM-DD

📊 TRACXN / FUNDING PROFILE
Tracxn: [Score X/100 / Not listed / Gated] | Stage: [Seed / Series A / etc. / N/A] | Badges: [list or "None"]
Crunchbase: Employees: [range] | Total funding: [$X / Not disclosed] | Last round: [Series X — $X — Date — Lead investor — Tier 1/2/Bootstrapped]
BD signal: [funding stage implication for outsourcing receptivity]
Source(s): [URLs] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

🔀 M&A, FUNDING & OWNERSHIP
[Summary of recent M&A/funding or "No recent M&A activity found"]
GeM supplier: [Yes — active / No / Not checked]
PE ownership: [PE firm — stake — fund vintage year / Not applicable]
BD signal: [ownership/funding implication]
Source(s): [URL] | Confidence: HIGH/MED/LOW | Source date: YYYY-MM-DD

🧠 BD INTELLIGENCE BRIEFING

Things to Know:
• ...

Conversation Starters:
• ...

Trigger Signals:
• ...

Potential Objections:
• [Objection] → [Suggested response]

Next Best Action:
[Do X] — [because Y] — [contact: Named Director] — [via: LinkedIn InMail / phone / email] — [hook: specific research finding]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📝 DATA QUALITY
Overall: [e.g., 9/14 fields HIGH · 3 MED · 2 LOW]
Data gaps: [List any fields that hit retry limit, or "None"]
Oldest source: [YYYY-MM-DD]

Confidence key: HIGH = MCA-verified or 2+ independent sources · MED = single credible source or aggregator · LOW = estimated, partial, or unverified
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Multi-Agent Architecture

PARALLEL MODE

User confirms company (Step 1)
         │
         ▼ POST PROGRESS STATUS BOARD
┌─────────────────────────────────────────────────────┐
│    WAVE 1 — SPAWN SIMULTANEOUSLY                    │
│  Workers: 2, 3, 4, 5, 6, 6B, 7, 7B, 7C             │
│           8, 9*, 11, 12, 13, 14                     │
│  (*Step 9 waits for Step 8 internally)              │
│  Post ✓ update after each worker approved           │
└─────────────────────────────────────────────────────┘
         │ (each worker ↔ checker loop — 7 criteria)
         ▼ POST "Wave 1 complete. Spawning Steps 10 & 10B…"
┌─────────────────────────────────────────────────────┐
│    WAVE 2 — SPAWN AFTER ALL WAVE 1 APPROVED         │
│  Workers: 10 (KServe Fit), 10B (ICP Score)          │
│  Read approved outputs from Steps 2–9               │
└─────────────────────────────────────────────────────┘
         │ (Wave 2 ↔ checker loop)
         ▼
┌─────────────────────────────────────────────────────┐
│         ORCHESTRATOR                                │
│  Verify Steps 10 & 10B ran after Wave 1 complete    │
│  Assemble all approved sections in order            │
│  Validate completeness · Tally confidence           │
│  Collect RETRY_EXHAUSTED signals                    │
│  Render final BD Research Report                    │
└─────────────────────────────────────────────────────┘

SEQUENTIAL MODE

User confirms company (Step 1)
         │
    ┌────▼────┐
    │ Step 2  │ Worker → Checker validates → approved ✓
    └────┬────┘
    ┌────▼────┐
    │ Step 3  │ Worker → Checker validates → approved ✓
    └────┬────┘
        ...
    ┌────▼─────┐
    │ Step 15  │ Worker → Checker validates → approved ✓
    └────┬─────┘
         │
         ▼
  Orchestrator assembles and presents final report

Checker Instructions

When validating any Worker output, apply all seven criteria:

1. Source present? Every fact must have a URL or named document. No source → send back.

2. Source credible? Prefer official sources (MCA, company website, major publications) over anonymous forums or low-quality aggregators.

3. Recency? Is the data from the last 12 months? If older, is it noted in the report with a ⚠️?

4. Accurate? Does the data make internal sense? (e.g., a 2-year-old company cannot have 50 years of history)

5. Complete? Did the Worker answer everything the step requires, or are there gaps?

   For Step 8 specifically: Did the Worker produce all five sub-sections (A — Product Review, B — Service Review, C — Employee Review, D — General Company Review, E — Review Handling Methodology)? Sub-sections are not optional — if a sub-section has no data, it must say so explicitly (e.g., "No product reviews found"). Silent omissions → send back. Verbatim review excerpts (required in Section A's app sub-section only, except when [Gated] is documented; product reviews in A and all of B, C, and D use themes — not quotes) must be: in quotation marks, ≤40 words, attributed with date and source URL. Paraphrases dressed as quotes → send back. If the Worker has documented [Gated], verbatim excerpts are not required — accept without retry.

6. Source diversity? For high-stakes fields (Turnover, Directors, Head Office, Years in Existence), are there at least 2 independent sources? Two aggregators that both pull from MCA (e.g., Tofler + Zauba Corp) do not count as independent — MCA is the single source. If only one source exists, the field must be marked Confidence: MED or LOW, not HIGH. This isn't a blocker — it's a signal for the output.

7. BD relevance? Does this output answer "why should KServe reach out to this company now?" — not just what is factually true, but what is strategically actionable. A section that lists accurate data with no BD framing should be sent back: "Add a BD insight — what does this data signal for KServe's outreach opportunity?" This criterion applies most strictly to Steps 8, 10, 12, 14, and 15.

   For Step 8E specifically: The BD signal field must be an implication, not a description. "They respond to reviews" is not BD insight. Acceptable example: "Review responses are boilerplate and slow (>7 days) — signals understaffed or unstructured CS; KServe's Customer Service offering directly addresses this." If the BD signal reads as description only → send back. Response rate estimates must always reference a sample count (e.g., "based on 12 reviews examined") — not conditional on volume. "None detected" is always valid if platforms were checked. Section 8E defaults to Confidence: MED (methodology is inferred, not stated) unless a job posting or news article explicitly names a tool or process.

If any criterion fails, return to Worker with specific, actionable feedback: "The turnover figure has no source — find the MCA filing or a news article citing the exact revenue figure."

Max retries: 2. If the Worker cannot satisfy all criteria after 2 attempts, it must send this structured signal to the Orchestrator before approving:

RETRY_EXHAUSTED: [Step N] — [field name] — [reason data is incomplete or unavailable]

Then approve the best available output with this note in the report: ⚠️ [Field]: Best available data — [brief reason]

The Orchestrator collects all RETRY_EXHAUSTED signals and surfaces them in the Data Quality footer.

Conflicting sources: If sources disagree, defer to the most authoritative source using this hierarchy: MCA > official company website > major publications > aggregators. Report all versions with source label.

Common contradiction patterns to check proactively:

• Turnover figures that differ across sources — verify the financial year and entity (parent vs. subsidiary) before flagging
• Director names on MCA that differ from company website — recent appointments may not have propagated to MCA yet; report both and note the lag
• Founded year vs. MCA incorporation date — these legitimately differ (founding vs. legal registration); report both if they differ
• Branch count from website vs. Google Maps vs. LinkedIn employees by location — triangulate and report the range if inconsistent

Only approve when all seven criteria are met (or a ⚠️ note and/or RETRY_EXHAUSTED signal is included for genuinely unavailable data).

Orchestrator Instructions

After all 17 Workers complete and each Checker has approved:

1. Assemble all approved sections into the Output Format template in order
2. Before assembling Steps 10 & 10B (KServe Fit + ICP Score): verify that approved outputs from ALL of Steps 2–9 are present. If any Wave 1 step is still pending, wait. If a Step 10 or 10B Worker ran before Steps 2–9 were all approved, discard that output and re-request with the full approved Wave 1 context.
3. Validate: no field is blank, pending, or "TBD" without a "Not publicly available" statement or a ⚠️ flag
4. If any section is missing or incomplete, return to that step's Checker with a re-request before rendering
5. Collect all RETRY_EXHAUSTED signals received from Checkers. If any exist, populate the "Data gaps" line in the 📝 DATA QUALITY footer with: [Step N — field] — [reason] for each one. If none, write "None".
6. Tally confidence levels across all 14 sections and populate the "Overall" line in the DATA QUALITY footer (e.g., 9/14 HIGH · 3 MED · 2 LOW). Find the oldest source date across all sections and populate "Oldest source".
7. Render the final report for presentation to the user