AI integration services that ship.
Our AI integration services put LLM applications, AI agents and RAG systems into the products and workflows you already run. Measured by evals, guarded in production, with a cost per request you can predict.
Generative AI that ships, not demos
Most AI pilots stall after the demo. Ours go live. Each feature gets one clear job, an eval suite before launch, and a feature-flag rollout against a success metric you sign off on.
What our AI integration team delivers
Generative AI development end to end: agents, retrieval, evaluation and the LLMOps to run them. One accountable team owns the feature from prototype to on-call.
- ✓AI agent development: copilots, tool use, function calling
- ✓RAG development: ingestion, chunking, hybrid search, reranking
- ✓LLM application development on OpenAI, Anthropic and open models
- ✓Evaluation harnesses with golden sets and regression evals
- ✓Guardrails: PII redaction, policy checks, human-in-the-loop review
- ✓Fine-tuning and prompt optimization for accuracy and cost
- ✓LLMOps: cost-per-request, latency and drift monitoring
- ✓AI automation of back-office workflows with n8n
- ✓Vector search and embeddings infrastructure
- ✓Multi-agent orchestration with audit trails
From pilot to production AI
A written hypothesis, a working prototype in one sprint, then evals, telemetry and a staged rollout. You see progress every sprint, not after six months.
Hypothesis
One problem statement, one success metric, agreed in writing before anyone writes code. We check your data can support it.
Prototype
A thin working slice in one sprint. Real users try it, we score it against a first eval set, then decide go or no-go.
Productionize
Eval harness, guardrails, telemetry, and cost and latency SLOs. Rollout behind a flag with a tight feedback loop.
Operate
On-call, weekly eval re-runs, drift monitoring, and steady work to lower cost per request as usage grows.
Three ways to start AI integration
Scope, data readiness and the number of systems the AI touches drive cost. Start with a one-sprint pilot or go straight to a production build.
Discovery sprint
Validate the use case fast. A working prototype, a first eval set, and a written go/no-go.
- Stakeholder interviews
- Data readiness audit
- Working prototype
- Eval harness
- Written recommendation
Production build
Ship a production-grade AI feature end to end, including guardrails, monitoring and rollout.
- Full LLM or RAG pipeline
- Latency & cost SLOs
- Evals + guardrails
- Feature-flag rollout
- Runbook handover
Embedded team
A pod of AI/ML engineers working alongside yours, shipping continuously against a rolling roadmap.
- 1–3 engineers
- Tech-lead overlay
- Friday demos
- Shared on-call
- Quarterly OKRs
AI integration, questions answered
What does an AI integration project cost?
Cost follows scope. The main drivers are data readiness, how many systems the AI connects to, how deep the evals and guardrails go, and your compliance bar. A discovery sprint gives you a working prototype and a written recommendation before you commit to a full build.
How long does it take to ship an AI feature?
A discovery sprint takes one sprint and ends with a go/no-go. Production timelines follow scope: our LLM prior-auth pipeline went from discovery to general availability in 14 weeks, with a soft launch to 5% of intake along the way.
Which LLMs and frameworks do you use?
OpenAI, Anthropic and open models, chosen per use case against your own eval set: accuracy, latency, cost per request and data-handling terms. Retrieval runs on pgvector or Pinecone, with orchestration in Python or TypeScript.
How do you keep AI output accurate and safe?
Every feature ships with an eval suite, guardrails for PII and policy, and human-in-the-loop review where the stakes call for it. After launch we re-run evals weekly and monitor drift, latency and cost.
Not sure which engagement fits? Tell us what you’re building and we’ll reply within one business day with a recommended starting point.
Often paired with AI integration
Most engagements blend two or three disciplines. These are the most common pairings.
Cloud & MLOps
AWS, GCP and Azure infrastructure for AI workloads: GPU inference, LLMOps pipelines and cost you can predict.
QA & LLM evals
Automated testing for your whole stack, plus LLM evaluation and red-teaming for the AI inside it.
UI/UX for AI
Interfaces that make AI output clear, correctable and trusted, designed with engineers in the room.
Proof in production: our LLM prior authorization case study auto-routes 87% of cases at 96% accuracy. We bring the same eval-first approach to healthcare AI solutions and fintech AI solutions, and write up the methods in our AI engineering insights.
Ready to ship your AI feature?
We respond within one business day, with a real human and a real opinion.