Add AI to the product you already have — not a new one
Drop production LLM features into your SaaS, mobile app, or internal tool. Model routing, prompt caching, eval suites, and the cost discipline you'd expect from senior engineers.
What “AI integration” actually means
Half the SaaS companies asking us about AI don’t need a new product. They need three or four well-chosen LLM features inside the product they already ship — semantic search, summary, generation, or a focused assistant. That’s integration work, not a greenfield build.
Done right, it’s about good API engineering on a stack you already use. Done wrong, it becomes a $50K/month OpenAI bill, a latency nightmare, and a feature users don’t trust. We do it right.
Six things we integrate well
The features we've shipped enough times to skip the rookie mistakes.
Semantic search
Vector + keyword hybrid search across your content. 10x better recall than keyword-only; works out of the box on most stacks.
Summarisation
Long doc / meeting / thread → short briefing. Map-reduce pattern handles 100-page inputs cleanly.
Drafting + generation
Email/proposal/spec drafts inside your app. Trained on your tone via in-context examples — not a chatbot bolt-on.
Classification & tagging
Cheap, fast tagging of incoming tickets, emails, transactions. Often 50x cheaper than humans, 95%+ accuracy.
Model routing
Easy queries → small fast model. Hard queries → Sonnet / GPT-4. Same UX, dramatically lower bill.
Eval suites
Automated quality tests against a labelled set of your real prompts. Every release runs through them before merge.
Where integration compounds
Patterns where AI inside an existing product moves the metric that matters.
Search inside your product
- Replace clunky keyword search with semantic recall
- Power-users find what they need in seconds, not minutes
- Drives feature adoption + retention measurably
Content + copy assistance
- Draft emails, subject lines, ad copy inside your app
- Trained on the user's past content for tone
- Boosts free-to-paid conversion by removing the blank-page problem
AI inside your CRM
- Auto-summarise customer threads and calls
- Draft follow-up emails grounded in real context
- Detect risk signals (churn, urgency) automatically
From API key to production feature
Most integrations ship in 3-6 weeks. Faster if your data is clean; longer if it's in 14 places.
Week 1 · Feature scoping
Pick the ONE feature with the highest ratio of "user pain solved" to "engineering effort". Define the eval set.
Week 2 · Build + benchmark
Ship the integration. Benchmark 3-4 model + prompt combos against the eval set. Pick the winner.
Week 3 · Tune cost + latency
Cache prompts, route to small models where possible, batch where it helps. Cut cost 30-70% from baseline.
Week 4 · Ship + monitor
Behind a feature flag at first. Real-user dashboards live. Iterate on the actual usage data.
What product teams ask before we ship
The questions that decide whether the feature actually launches.
01 Do we really need to build, or can we just buy?
For commodity features (basic chat widget, generic AI search bar) — buy. For features that are core to your product’s value, custom integration usually wins on cost, control, and differentiation within 12 months.
02 How do we keep our OpenAI bill from exploding?
Three levers: model routing (cheap models for easy queries), prompt caching (repeat structures cached), and aggressive request batching. We routinely cut LLM spend 50%+ vs. a naive integration.
03 What about latency — won't users complain it's slow?
Streaming responses fix the perception problem. For workloads where streaming isn’t an option, we use smaller/distilled models or pre-compute where the access pattern allows. p95 under 1s is the bar we aim for.
04 How do we evaluate quality before shipping?
Labelled eval set built from your real use cases (100-500 examples). Every model + prompt change runs through it. We treat LLM features the same way we’d treat a search ranker — with metrics, not vibes.
05 Can you work with our existing engineering team?
Yes. Most engagements are 50/50 — we ship the AI-specific parts (model routing, eval suite, prompt management) while your team handles UI and integration. You own the code.
What teams say after going live with AI integrations
What feature should AI handle inside your product?
Two-minute form. We reply within 4 working hours.