AI Integration Services That Actually Ship to Production

Claude, OpenAI, and open-source model integration. RAG pipelines, agentic systems, evals, and the guardrails your product needs to stay correct under load. Founder-led — we tell you straight when a feature does not need AI.

Past the demo, into production

Every B2B product has a Slack channel full of AI demos that worked once in a Loom recording and never made it into the product. The pattern is consistent — a senior engineer wires up the OpenAI SDK over a weekend, the demo is great, the team puts it on the roadmap, and then production reveals the problems. Token costs balloon. The model hallucinates in ways the demo never exposed. Edge cases go uncovered because there is no eval harness. The feature ships, breaks for a real customer, and gets quietly turned off.

AI integration services are the engineering layer that turns a working demo into a feature you can put your product reputation behind. Structured outputs, schema validation, evaluation harnesses, prompt caching, retrieval-augmented generation, model routing, fallback paths, audit logging — the unsexy parts that decide whether your AI feature stays on or gets turned off.

What we build

Claude API integration — Opus and Sonnet, with prompt caching, tool use, and extended thinking
OpenAI integration — GPT-4o, GPT-5, structured outputs, function calling, and the Realtime API
Retrieval-augmented generation (RAG) pipelines with hybrid keyword + vector retrieval and citations
Vector database setup on pgvector, Pinecone, Qdrant, or Weaviate depending on scale and budget
Agentic systems with tool use, state management, and the right hand-off model for your domain
Evaluation harnesses with regression tests, golden datasets, and human-graded sample queues
Structured outputs with JSON schema validation, retries, and fallback paths when the model fails
Prompt management — versioning, A/B testing, and rollout controls so prompt changes are deployable
Model routing — cheap model first, premium model on escalation, with cost and latency tracking
Open-source model deployment on vLLM, Ollama, or Together AI when self-hosted is the right call

Our methodology

One-week AI feasibility audit first. We talk to the team, look at the data, write a one-page document that says either "this is an AI problem worth solving" or "this is a database query and a regex." That audit is paid separately at $2,500 and you keep the doc either way.

When the answer is yes, we run a two- to twelve-week build with an eval harness from day one. We do not ship AI features without a baseline eval — too many demos pass vibes-based testing and break the moment a real customer types something unexpected. Production launch is gated by eval scores against the regression set, not founder enthusiasm.

Process & timeline

Week 1: AI feasibility audit — data review, baseline eval, written recommendation
Week 2-3: Prototype — first prompt iteration, structured outputs, basic retrieval if RAG-based
Week 4-8: Hardening — eval harness, regression suite, prompt caching, model routing, audit logs
Week 9-12: Production launch — staged rollout, monitoring, cost dashboards, on-call runbook
Optional retainer: ongoing prompt tuning, model upgrades, eval expansion, new feature integration

Tech & tools

Claude API + SDK

OpenAI API + SDK

Anthropic Prompt Caching

pgvector + Pinecone

LangChain + LlamaIndex

Vercel AI SDK

PromptLayer + Braintrust

Ollama + vLLM

Inngest + Trigger.dev

Layered on top of our standard API development stack and SaaS platform backbone. The AI is one tier of the system, not the whole product.

Reference builds

AI integration shows up across our production work. J5 Sales OS uses LLM-driven contact enrichment, outreach personalization, and a curated lead-scoring loop. A contractor estimating engine uses Claude to draft proposal language from job scoping notes. Wilder Recovery uses AI-assisted intake and content workflows.

We dogfood our own architecture before we ship it to clients. Every prompt cache strategy, eval harness pattern, and RAG retrieval approach we recommend has run against our internal workloads first. No experiments paid for by client projects.

AI integration services served from Macon, GA, with clients across Atlanta, Austin, San Francisco, and beyond.

Pricing

Fixed-fee per scope. Typical ranges:

One-week AI feasibility audit with written recommendation: $2,500 flat
Single AI feature added to an existing product (structured outputs, evals, caching): $8k – $22k
Full RAG pipeline with vector DB, retrieval evals, and admin tooling: $18k – $48k
Agentic system with tool use, state management, and production guardrails: $30k – $80k
AI-native product MVP — full SaaS surface plus AI core: $55k – $95k

30-day post-launch support included. Optional retainer for ongoing prompt tuning, model upgrades, and eval expansion.

What you get

Full source code repository in your GitHub organization
Eval harness with regression tests, golden datasets, and a human-grading queue
Prompt templates versioned and deployable independently of code releases
API keys in your Anthropic and OpenAI accounts — no shared credentials
Cost dashboard with per-feature, per-customer, and per-prompt attribution
Audit logs for every AI-generated output — input, output, model version, cost, latency
Production runbook for the top failure modes and how to triage them
Optional retainer for ongoing AI work and platform upgrades

FAQs

Claude or OpenAI — which one should we use?

Default to Claude for long-context work, structured outputs, and agentic tool use — particularly Claude Opus and Sonnet for production reliability. OpenAI when you need specific GPT-4o capabilities, real-time voice, or you have an existing infrastructure investment. We frequently ship hybrid setups where the router picks the right model for each task.

What is RAG and do we need it?

Retrieval-augmented generation lets the model answer questions against your private documents, knowledge base, or database without retraining. You need it when your AI feature has to be grounded in specific business data — support agents, internal search, document review, sales enablement. We build RAG pipelines with hybrid keyword + vector retrieval, chunk-level citations, and an eval harness.

How do you handle hallucinations and correctness?

Three layers. Structured outputs with JSON schema validation so the response shape is guaranteed. Citations from retrieved documents so claims are auditable. An evaluation harness with regression tests and human-graded samples so we catch drift before users do. AI features without these layers are demos, not products.

What does prompt caching actually save us?

Anthropic prompt caching cuts the cost of large system prompts and retrieved-context payloads by up to 90% on cache hits and significantly reduces time-to-first-token. We design every Claude integration to cache aggressively — system prompt, tool definitions, retrieved documents — so per-request cost stays predictable as you scale.

Do we own the AI integration code?

Completely. You get the GitHub repository, the prompt templates, the eval harness, and the deployment configuration. The API keys are in your accounts. You can switch providers, swap models, or move on-prem without rebuilding the surrounding system.

Engineering reading

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AI Integration Services — Where We Serve

Georgia-based engineering team, working with AI-curious clients across 14 US metros. Discovery and build run remotely; in-person prompt-engineering sessions easy to schedule in Atlanta and the Southeast.