--- name: find-ai-consultancy description: Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers. license: MIT metadata: api_base: https://api.servicegraph.co industry: data_ai_consulting version: "0.2" --- # find-ai-consultancy Drive the **ServiceGraph API** (`https://api.servicegraph.co`) to find, shortlist, and enrich US AI/ML and data consultancies. The catalog tags firms with `industry:data_ai_consulting` and a 4-tag service sub-taxonomy: `ai-ml-development` (the largest at ~12k firms), `data-analytics`, `cloud-services`, and `api-integration`. Confirm exact tag names via `/v1/tags` since taxonomy can drift between catalog releases. **Always pin `industry:data_ai_consulting`.** This skill exists to do that automatically — the user shouldn't have to think about catalog taxonomy. Any HTTP client works (curl, fetch, requests). Examples below use curl. ## Sibling skills — defer when scope is different - **General application or backend dev that just uses AI as a feature** (e.g. "build us a SaaS with an AI chatbot tab") → `find-software-developer`. This skill is for engagements where the AI/ML/data work IS the deliverable. - **Web/site projects that include some AI** → `find-web-developer`. - **AI-related marketing or content** → `find-marketing-agency`. If the user wants AI/ML/data engineering as the primary deliverable (model building, pipelines, agents, MLOps, BI), this skill applies. ## When NOT to use this skill - **Consumer AI courses or learning** ("find me an online course to learn ML") — out of scope; the catalog is firm-procurement. - **AI/LLM product comparisons** ("ChatGPT vs Claude vs Gemini", "Cursor vs Copilot") — software-product questions, not procurement. - **DIY/code tasks** ("how do I fine-tune Llama", "review this PyTorch training loop"). - **In-house ML/data hires** (Machine Learning Engineer, Data Scientist, ML Platform Engineer). - **Generic AI knowledge** ("explain how transformers work"). - **Non-US firms.** - **Individual freelance ML engineers / data scientists.** If the user is a *business* procuring external AI/ML/data services, this skill applies — defaults to fire on B2B procurement intent. ## The four-tier funnel | Tier | Auth | Cost | Use it for | |---|---|---|---| | `GET /v1/tags` | none | free | **First call of every session.** Discover legal field names, kinds, operators, values. | | `GET /v1/check?filter=...` | none | free | Validate a filter before spending an explore/search call. | | `GET /v1/explore?filter=...` | none | free, IP-throttled | Scope: count + breakdowns. Use to size the candidate pool before quota-spending. | | `GET /v1/search?filter=...` | bearer | 200 unique firms / month free | Brief firm cards. **No url, no contact info.** Use for ranking / shortlisting. | | `GET /v1/get/:id` | bearer | 50 unique firms / month free | Full bundle: url, phone, email, social, legal name, address. **Only call for shortlisted firms.** | | `POST /v1/research` | paid | not in MVP | Deferred — skip. | **Quota rule that matters**: `/search` and `/get` charge per *unique firm viewed per calendar month*, not per call. Re-paging the same query is free. Two different filters that overlap charge once for the overlap. Re-fetching a firm you already pulled this month is free. ## Session-start ritual Before constructing any filter, call: ``` GET https://api.servicegraph.co/v1/tags?include_values=1 ``` Cache the response for the conversation. Confirm `data_ai_consulting` is present in the `industry` value list. The relevant sub-tags under `service_provided` are `ai-ml-development`, `data-analytics`, `cloud-services`, and `api-integration` — verify exact names before constructing filters with `service_provided:` predicates. Field kinds you'll use most: - **categorical**: `industry` (always `data_ai_consulting`), `state`, `pricing_model`, `company_size_signal`, `geography_served` — op `:` - **tag_set_with_evidence**: `service_provided` (e.g. `ai-ml-development`, `data-analytics`, `cloud-services`, `api-integration`) — op `:` with optional `@evidence` - **numeric**: `rating`, `review_count_total`, `founded_year` — ops `= >= <= > <` - **presence**: `has:phone`, `has:clutch`, `has:rating`, `has:linkedin_company`, … - **keyword**: free-text substring across firm name / brand / title / meta / legal_name. **Sub-niches like RAG, LLM, MLOps, computer vision, NLP, recommendation systems are typically keyword-only.** ## Auth `/tags`, `/check`, and `/explore` are anonymous. `/search` and `/get` require a bearer token. **Resolution rule** — try these sources in order before triggering OTP: 1. **Shell environment**: `$SERVICEGRAPH_TOKEN`. Most agent harnesses only inherit explicit `export`s, not dotenv files — so this catches the case where the user has it exported in `~/.bashrc` / `~/.zshrc`. 2. **Project dotenv files**: read `.env.local` then `.env` in the current working directory and look for a `SERVICEGRAPH_TOKEN=…` line. **This is the common case the agent will miss otherwise** — users frequently put the token in `.env.local` (gitignored) and expect it to "just work," but Claude Code and similar harnesses don't auto-load dotenv files. If you find it, use it; don't ask. If found in any of the above, set `Authorization: Bearer ` on every authed request and skip OTP. 3. **Otherwise, walk the user through OTP** (one-time, ~30 s): - Ask the user for their email address. - `POST /v1/auth/request-otp` with `{"email": "..."}`. Returns 204; a 6-digit code lands in their inbox. - Ask the user to paste the code. - `POST /v1/auth/verify-otp` with `{"email": "...", "code": "...", "name": ""}`. Returns `{"token": "vk_...", "expires_at": "...", "user": {...}}`. - Use that token for the rest of the session. - Tell the user: *"Save this as `SERVICEGRAPH_TOKEN` to skip this step next time — either `export SERVICEGRAPH_TOKEN=…` in your shell rc, or add `SERVICEGRAPH_TOKEN=…` to a `.env.local` file in your project (gitignored). The token is shown once and lasts 90 days."* If a `/search` or `/get` returns 401 mid-session, the token expired or was revoked — re-run the OTP flow. ```bash # 1. trigger the email curl -X POST 'https://api.servicegraph.co/v1/auth/request-otp' \ -H 'Content-Type: application/json' \ -d '{"email":"you@example.com"}' # 2. exchange the code curl -X POST 'https://api.servicegraph.co/v1/auth/verify-otp' \ -H 'Content-Type: application/json' \ -d '{"email":"you@example.com","code":"123456","name":"my-cli"}' # → { "token": "vk_…", "expires_at": "...", "user": {...} } ``` ## Filter DSL One query parameter, GitHub-search-style. ``` filter := orExpr orExpr := andExpr ("OR" andExpr)* andExpr := notExpr (("AND")? notExpr)* # whitespace = implicit AND notExpr := ("NOT" | "-") notExpr | atom atom := "(" filter ")" | predicate predicate:= IDENT op valueOrList | bareword op := ":" | "=" | ">=" | "<=" | ">" | "<" valueOrList := value ("," value)* value := IDENT | NUMBER | tagAtEvidence tagAtEvidence := IDENT "@" ("low"|"medium"|"high") bareword := IDENT | NUMBER # → keyword: ``` **Four rules that bite:** 1. **AND binds tighter than OR.** `a OR b c` parses as `a OR (b AND c)`. Use parens. 2. **Comma list = OR within one predicate.** `state:CA,NY,TX` matches any of the three. 3. **Negation is `-x` or `NOT x`.** Negative literals inside a comma list are **not** allowed: `state:CA,-NY` is rejected. Use `state:CA -state:NY`. 4. **Bareword = keyword search.** Any IDENT or NUMBER not followed by an operator becomes a free-text substring across name / brand / title / meta / legal_name. Multiple barewords AND. **AI-flavored examples** (validate yours with `/v1/check`): ``` industry:data_ai_consulting service_provided:ai-ml-development industry:data_ai_consulting service_provided:ai-ml-development@high state:CA industry:data_ai_consulting service_provided:data-analytics pipelines industry:data_ai_consulting llm rag industry:data_ai_consulting computer vision healthcare industry:data_ai_consulting mlops industry:data_ai_consulting (service_provided:ai-ml-development OR service_provided:data-analytics) industry:data_ai_consulting service_provided:ai-ml-development@high rating>=4 has:clutch ``` When in doubt, hit `/v1/check?filter=...` first. **Sub-niche → keyword/tag mapping**: | User asks for | Use | |---|---| | AI/ML model building | `service_provided:ai-ml-development` | | Data engineering / pipelines | `service_provided:data-analytics` + keywords `pipelines`/`engineering` (no `data-engineering` tag exists) | | BI / analytics | `service_provided:data-analytics` (covers BI too — no separate `business-intelligence` tag) | | Cloud architecture for data/ML | `service_provided:cloud-services` | | API integration / data integration | `service_provided:api-integration` | | LLM apps / RAG / agents | `llm`, `rag`, `agent` (keywords) | | Generative AI | `generative ai`, `genai` (keywords) | | Computer vision | `computer vision`, `cv` (keywords) | | NLP / IDP / document understanding | `nlp`, `idp`, `document understanding` | | MLOps / model deployment | `mlops`, `deployment` | | Recommendation systems | `recommendation`, `recsys` | | Predictive analytics / churn / forecasting | `predictive`, `forecasting`, `churn` | ## firm_id contract `firm_id` is a stable 12-hex-char handle: ``` firm_id = sha256(apex.lower().rstrip(".")).hexdigest()[:12] ``` ```python import hashlib def firm_id(apex): return hashlib.sha256(apex.lower().rstrip(".").encode()).hexdigest()[:12] ``` ```bash echo -n "scaleai.com" | tr 'A-Z' 'a-z' \ | openssl dgst -sha256 -hex | awk '{print substr($2,1,12)}' ``` ## Recipes ### A. AI/ML consultancy for a recommendation engine User: *"AI/ML consultancy to build our recommendation engine for an ecommerce site."* ``` GET /v1/explore?filter=industry:data_ai_consulting+service_provided:ai-ml-development+(recommendation OR recsys)+ecommerce GET /v1/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development+recommendation+ecommerce&limit=10 GET /v1/get/ # ×3 ``` ### B. RAG / LLM consultancies for a chatbot User: *"Three RAG/LLM consultancies for an enterprise chatbot."* ``` GET /v1/search?filter=industry:data_ai_consulting+(rag OR llm)+chatbot+enterprise ``` If thin, drop `enterprise` and surface client-tier signals from `/get` after. ### C. Data engineering partner User: *"Data-engineering partner to build our analytics pipelines."* The catalog has no `data-engineering` tag — `data-analytics` is the closest sub-tag and it covers both BI and engineering work. Pin the tag and add keywords for the engineering flavor: ``` GET /v1/search?filter=industry:data_ai_consulting+service_provided:data-analytics+(pipelines OR engineering) ``` ### D. MLOps for model deployment User: *"MLOps consultancy to help us deploy models to production."* ``` GET /v1/search?filter=industry:data_ai_consulting+mlops ``` ### E. Indirect intent — "use AI to predict customer churn" User: *"We want to use AI to predict customer churn — who can help us build that?"* That's a custom-ML consulting ask in the predictive-analytics niche: ``` GET /v1/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development+(churn OR predictive) ``` If the user gave a vertical (SaaS, retail, telco), add it as a keyword. ### F. Computer vision + healthcare vertical User: *"AI consultancies specializing in computer vision for healthcare."* ``` GET /v1/search?filter=industry:data_ai_consulting+computer vision+healthcare ``` ### G. Quality threshold + Fortune 500 clients User: *"Three AI/ML consulting firms with 4-star ratings and Fortune 500 clients."* ``` GET /v1/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development@high+rating>=4&limit=10 ``` The "Fortune 500" angle isn't structured — surface from briefs and let the user pick, or add `fortune` as a keyword. ### H. Custom LLM agent for customer service User: *"Custom LLM agent for our customer-service workflows."* ``` GET /v1/search?filter=industry:data_ai_consulting+(llm OR agent)+(customer service OR support) ``` ### I. BYO apex list — enrich domains the user already has User pastes 8–20 AI consultancy domains. For each: 1. Compute `firm_id` locally. 2. `GET /v1/get/` — full bundle if in catalog, 404 if not. 3. Aggregate, present, flag the not-found ones to the user. A 404 here often means the firm is actually a SaaS product company (many AI vendors brand as "AI services" but operate as a product) — not in the consulting catalog. ## Gotchas - **Always pin `industry:data_ai_consulting`.** Without it, `ai-ml-development` as a service tag could surface IT firms that list AI as a sub-service. - **Defer to `find-software-developer` for general dev that uses AI as a feature.** When the deliverable is a SaaS product or app and AI is one of several features, that's software-dev work; this skill is for engagements where AI/ML/data work IS the deliverable. - **Catalog audit notes**: AI/ML-tagged firms have a higher historical rate of mis-classification (some are SaaS products, some are B2C ed-tech). The catalog has been audited but residual leakage is possible. If a `/get` returns a SaaS product, the agent should flag this and skip rather than recommend. - **Many sub-niches are keyword-only.** Multi-word sub-niches split into ANDed barewords (`computer vision` → `computer` AND `vision`). - **LLM-product comparisons (ChatGPT vs Claude vs Gemini, etc.) are NOT procurement** — refuse those. - **AI courses for individuals (Coursera, fast.ai, Andrew Ng courses) are NOT in the catalog** — refuse those. - **`looks_not_pro_services` 404 is not a bug.** A `firm_id` may exist in `/search` but 404 on `/get` if it's been flagged. Skip and continue; not charged. - **`/v1/explore` k=20 suppression.** When fewer than 20 firms match, the response is `{"count": "<20", "suppressed": true, "breakdowns": {}}`. Drilling further makes the count smaller. Broaden or escalate to `/v1/search`. - **Briefs from `/search` do NOT include `apex`, `url`, `phone_primary`, `email_primary`, `legal_name`, or address.** If the user asks for contact info, you must `/get/:id`. - **Quota is per-user-per-month, deduped on first view.** Re-views are free; re-pagination is free. ## Errors All errors return JSON: `{"error": {"code": "...", "message": "..."}}`. | Status | Code | What to do | |---|---|---| | 400 | `filter_parse_error` | Payload includes `position`. Fix the filter, re-validate with `/v1/check`. | | 400 | `filter_required` | Empty filter where one is required. | | 400 | `invalid_firm_id` | firm_id must be 12 lowercase hex chars. Re-derive. | | 401 | `unauthorized` | Token missing/expired. Re-run OTP. | | 404 | `not_found` | Firm not in catalog or flagged. Not charged. Skip and continue. | | 429 | `rate_limited` | Honor `Retry-After` header / `retry_after` field. | | 429 | `monthly_quota_exhausted` | Switch to `/v1/explore`-only mode for the rest of the month. Tell the user. | ## End-to-end example User: *"Three AI/ML consultancies to build a recommendation engine for an ecommerce site, ideally with 4-star ratings and Fortune 500 clients."* ``` GET /v1/tags?include_values=1 GET /v1/check?filter=industry:data_ai_consulting+service_provided:ai-ml-development@high+(recommendation OR recsys)+ecommerce+rating>=4 GET /v1/explore?filter=industry:data_ai_consulting+service_provided:ai-ml-development@high+(recommendation OR recsys)+ecommerce+rating>=4 GET /v1/search?filter=...&limit=10 GET /v1/get/ # ×3 ``` End of session: report `X-Quota-Remaining-Month`.