Book a ConsultationAI & LLM Integration for Existing Products
Add AI features to a product that already has real users — search, summarization, structured extraction, or an assistant grounded in your own data, integrated by the same engineer who builds the rest of your stack.
Most AI integration work isn't about the model — it's about the plumbing around it: getting the right context in front of it, validating what comes back, handling failure and latency gracefully, and not blowing the API budget on every page load. I treat an LLM call like any other external dependency: designed for, not just wired up.
This is for teams that already have a product and want to add AI to a specific, well-defined part of it — not a rebuild, and not a general-purpose chatbot dropped into the corner of the screen.
Who this is for
- Product teams adding AI-assisted search, summarization, or content generation to an existing app
- Founders who need a retrieval-augmented assistant grounded in their own data, not a wrapper around a public model
- Teams that tried a quick integration themselves and need the reliability and cost side engineered properly
What you get
Retrieval pipeline
Chunking, embeddings, and vector search set up around your actual data, with a clear story for keeping it fresh.
Prompt and output design
Structured, validated model output your application code can trust — not raw text parsed with regex.
Cost and latency controls
Caching, streaming, and model selection tuned so AI features don't quietly become your largest infrastructure bill.
Guardrails
Input handling and output checks appropriate to what the feature touches — support tooling and financial data get different treatment.
Observability
Logging on prompts, responses, and failures, so you can see what the model is actually doing in production.
How the engagement runs
- 01
Scoping call
The specific problem the AI feature needs to solve, and what a wrong answer costs you.
- 02
Design
Data flow, model choice, and failure handling decided before implementation starts.
- 03
Build
Shipped against your real data, not a curated demo set.
- 04
Evaluation
Tested against real inputs and edge cases before it reaches users.
- 05
Launch and monitor
Deployed with logging in place so quality and cost are visible, not assumed.
Engagement options
MVP Sprint
Idea to shipped product, fast
4–8 weeks
Founders who need a real, usable v1 in front of users this quarter.
- Scoping workshop and a written technical plan
- Production build — app, API, database, auth, deployment
- CI/CD and a live deployment you own
- Regular demo checkpoints, async updates in between
A single, well-scoped AI feature is typically a shorter engagement than a full product build.
Monthly Retainer
An embedded senior engineer, minus the hiring cycle
Rolling, one-month minimum
Teams shipping continuously who need senior capacity without a headcount req.
- A fixed block of engineering hours each month
- Feature delivery, code review, and refactors
- Works inside your existing tools — Slack, board, standups
- Cancel or pause with notice
Architecture Audit
An honest read on what's already built
1–2 weeks
Teams inheriting a codebase, pre-fundraise diligence, or diagnosing 'why is this so slow'.
- Codebase, infrastructure, and cost review
- Written findings ranked by risk and effort
- A prioritized remediation plan
- A walkthrough call to go through the findings
Frequently asked questions
Do you build the AI model, or integrate an existing one?
Integrate. I work with existing foundation model APIs (OpenAI, Anthropic, and similar) rather than training models from scratch — that's the right tradeoff for the vast majority of product use cases.
Can you make an AI feature answer only from our own data?
Yes — that's a retrieval-augmented setup: your content is chunked, embedded, and searched, and the model is constrained to answer from what's retrieved rather than its general training.
How do you keep AI features from getting expensive?
Caching repeated queries, choosing the smallest model that meets the quality bar, and streaming responses instead of paying for output nobody reads. Cost is a design constraint from the start, not a surprise at the end.
Can you audit an AI feature we already built?
Yes, that fits the architecture audit engagement — a review of prompt design, cost, and reliability with a prioritized list of what to fix.
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Ready to talk about ai & llm integration?
Book a call or send a message — you'll hear back within a day.