find-ai-consultancy

find-ai-consultancy

Drive the ServiceGraph API (https://api.servicegraph.co) to find, shortlist, and enrich US AI/ML and data consultancies via the pro_services dataset. 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. There is no data-engineering or business-intelligence sub-tag — data-analytics covers both. Confirm exact tag names via /v1/datasets/pro_services/fields?include_values=1.

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 uses AI as a feature

(e.g. "build us a SaaS with an AI chatbot tab") → find-software-developer.

• Web/site projects that include some AI → find-web-developer.
• AI-related marketing or content → find-marketing-agency.

This skill is for engagements where the AI/ML/data work IS the deliverable.

When NOT to use this skill

• Consumer AI courses or learning ("find me an online course to learn ML").
• AI/LLM product comparisons ("ChatGPT vs Claude vs Gemini", "Cursor vs Copilot").
• DIY/code tasks ("how do I fine-tune Llama", "review this PyTorch loop").
• In-house ML/data hires (Machine Learning Engineer, Data Scientist).
• Generic AI knowledge questions.
• Non-US firms / individual freelance ML engineers.

MCP server (preferred for authed calls)

If your harness has the ServiceGraph MCP server loaded (tools containing servicegraph), prefer those — OAuth 2.1 + PKCE keeps the token in the harness sandbox. Otherwise use the REST flow below.

API surface (dataset id: pro_services)

Every endpoint requires the bearer (Authorization: Bearer vk_…). No anonymous tier.

| Endpoint | Cost | Use it for | |---|---|---| | GET /v1/datasets/pro_services/fields[?include_values=1] | free | Confirm data_ai_consulting industry value and sub-tag names. | | GET /v1/datasets/pro_services/check?filter=… | free | Validate filter. | | POST /v1/datasets/pro_services/translate-intent | free | {intent} → DSL filter + sanity count. | | GET /v1/datasets/pro_services/search?filter=…&limit= | free | Brief firm cards + per-row unlock hint + total. | | GET /v1/datasets/pro_services/:apex | free | One row brief; detail only if unlocked. | | POST /v1/datasets/pro_services/unlocks | 10 credits / firm | {apexes:[...]} ≤100; atomic; 30-day TTL on detail. | | GET /v1/me/credits | free | Balance. |

Cost model. Discovery / validation / search / brief reads are free. Detail (url, phone, email, social, address, full platforms map) costs 10 credits per firm and lasts 30 days.

Auth

vk_* API keys minted in the dashboard. Keep the token out of the LLM context — never read .env* into your context; dispatch via shell.

1. Try the call first through a shell wrapper that sources

.env.local:

``bash ( set -a; [ -f .env.local ] && . ./.env.local; set +a; curl -sS -H "Authorization: Bearer $SERVICEGRAPH_API_KEY" \ 'https://api.servicegraph.co/v1/datasets/pro_services/fields' ) ``

1. On 401 prompt the user (don't accept the key in chat):

> "Open https://servicegraph.co/profile/api-keys, create a > key, and add SERVICEGRAPH_API_KEY=vk_… to .env.local here > (or export it). Tell me when done. Please don't paste the key > into chat."

1. Retry after the user signals ready.

Filter DSL

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:<bareword>

Four rules that bite: AND binds tighter than OR (use parens); comma list = OR within one predicate; negation is -x or NOT x; bareword = keyword search (quote multi-word phrases).

AI-flavored examples (validate yours with /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

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) | | BI / analytics | service_provided:data-analytics (covers BI too — no separate business-intelligence tag) | | Cloud architecture for data/ML | service_provided:cloud-services | | API / data integration | service_provided:api-integration | | LLM apps / RAG / agents | llm, rag, agent (keywords) | | Generative AI | "generative ai", genai | | Computer vision | "computer vision", cv | | NLP / IDP / document understanding | nlp, idp, "document understanding" | | MLOps / model deployment | mlops, deployment | | Recommendation systems | recommendation, recsys | | Predictive analytics / churn / forecasting | predictive, forecasting, churn |

Identifying firms — apex

Firms are identified by their apex domain (scaleai.com, not www.scaleai.com/about).

Recipes

A. AI/ML consultancy for a recommendation engine

User: "AI/ML consultancy to build our recommendation engine for an ecommerce site."

GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development+recommendation+ecommerce&limit=10

# Present, get pick of 3. "Unlocking 3 = 30 credits, 30-day TTL."
POST /v1/datasets/pro_services/unlocks
  { "apexes": ["firm-a.com", "firm-b.com", "firm-c.com"] }

B. RAG / LLM consultancies for a chatbot

User: "Three RAG/LLM consultancies for an enterprise chatbot."

GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+(rag OR llm)+chatbot+enterprise&limit=10

If thin, drop enterprise and surface client-tier signals from the unlocked detail later.

C. Data engineering partner

User: "Data-engineering partner to build our analytics pipelines."

No data-engineering tag — data-analytics is the closest and covers both BI and engineering. Pin the tag plus keyword:

GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:data-analytics+(pipelines OR engineering)&limit=10

D. MLOps for model deployment

GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+mlops&limit=10

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?"

GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development+(churn OR predictive)&limit=10

Or let the translator do the mapping:

POST /v1/datasets/pro_services/translate-intent
  { "intent": "AI consultancy to build customer churn prediction" }

F. Computer vision + healthcare vertical

GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+"com