Client AI integration work · APIs, webhooks & validation · Human-led delivery
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AI integrations, APIs & webhooks case studies

Two client integrations where data contracts, validation and failure handling matter as much as the AI.

These case studies are based on integration work delivered for clients connecting AI to forms, CRMs and approved knowledge services. They show explicit field mapping, structured outputs, validation, fallback behavior and technical handover.

Case Study A · Form to CRM integration

Website form → webhook → AI summary → CRM record → internal email

A client needed free-text inquiries transformed into structured CRM records while retaining the original submission for follow-up and auditability.

Starting stackWebsite form + webhook + AI API + CRM
Main riskSchema mismatch and invented field values
Primary goalValidated structured enrichment
Delivery patternData mapping + API integration + QA

Starting problem

Lead information arrived in free text. Earlier direct integration behavior produced inconsistent categories, optional fields caused failures and downstream validation errors were difficult to diagnose.

Constraints

  • Raw form data had to remain unchanged and independently available.
  • AI-generated fields needed a strict schema and predefined category values.
  • Missing source data had to remain unknown rather than inferred.
  • CRM/API failures required a visible retry or manual-review path.

A→Z implementation

  1. Define interface contractsDocumented form payload, webhook body, AI request, AI response schema and CRM field requirements including types and allowed values.
  2. Build transformation schemaConfigured the AI to return only requested fields such as summary, category, urgency, service need and explicit unknowns.
  3. Add validation layerValidated JSON shape, enums, required fields and payload size before allowing any CRM write.
  4. Connect downstream actionsCreated or updated the CRM record, preserved raw source text, attached AI enrichment and triggered the internal notification.
  5. Test integration failuresSimulated empty fields, malformed payloads, AI timeout, invalid JSON, CRM errors and duplicate webhook delivery.
  6. Document operationsDelivered endpoint/data maps, error handling, retry behavior, manual fallback and failed-run inspection guidance.

Artifacts delivered

End-to-end data contract
AI structured-output schema
CRM field mapping table
Validation/error-flow diagram
Integration test matrix
Operations/retry runbook

Acceptance criteria

  • Valid submissions created correctly mapped CRM records.
  • Raw source data remained separate from AI-generated enrichment.
  • Invalid AI output could not corrupt downstream fields.
  • Failed writes were visible and followed the agreed recovery path.
Case Study B · Knowledge API integration

Chatbot → approved knowledge endpoint → grounded answer with fallback

A client had current product/service information available through an API and needed the chatbot to retrieve approved live data instead of relying only on static prompt context.

Starting stackChatbot + AI model + REST knowledge API
Main riskBad tool calls or unsupported answers
Primary goalGround answers in approved current data
Delivery patternTool contract + grounding rules + QA

Starting problem

The chatbot could return outdated information as product/service details changed. The client needed live retrieval from approved endpoints without exposing authentication or pretending current data was available when a request failed.

Constraints

  • The assistant could call only approved read endpoints with approved parameters.
  • No answer could claim current data after a failed retrieval.
  • Authentication secrets had to stay outside user-visible prompt content.
  • Unexpected API responses needed a safe fallback.

A→Z implementation

  1. Define retrieval use casesMapped which questions required live lookup, which parameters were needed and which cases should remain static or escalate.
  2. Document endpoint contractSpecified method, endpoint, authentication handling, parameters, response fields, timeout and error behavior.
  3. Configure tool-selection instructionsDefined when the assistant should retrieve, what parameters were allowed and how missing data should be handled.
  4. Build grounding rulesRequired answers to use returned fields, avoid unsupported additions and state clearly when current data could not be retrieved.
  5. Test tool/API edge casesTested valid lookup, missing item, ambiguity, timeout, invalid parameter, malformed response and unrelated questions.
  6. Finalize security and handoverDocumented allowed endpoints, secret-storage expectations, test cases and schema-change procedures.

Artifacts delivered

Retrieval use-case matrix
API contract + parameter map
Tool-selection instruction rules
Grounded answer/fallback rules
API/tool-call QA matrix
Security + maintenance handover

Acceptance criteria

  • Questions requiring current data followed only the approved lookup path.
  • Answers reflected returned API data without inventing unavailable fields.
  • API failure produced a fallback instead of a false current answer.
  • Authentication material remained outside user-visible/model prompt content where architecture permitted.
Integration feasibility depends on third-party APIs, permissions, rate limits and authentication methods. Compatibility can change if external providers change their interfaces or access policies.
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