AI Integrations — Add LLM Features to Your Existing Product

Your competitors are shipping AI features. Your users are asking for them. But your team doesn't have months to spare on prompt engineering, eval design, vector DB plumbing, and cost monitoring. We add production-grade AI features to your existing SaaS or web app — search, copilots, summarisation, classification, extraction — without rewriting your stack.

The problem we kill

You don't need to "do AI." You need a single high-value feature that ships in weeks, makes your product feel modern, and doesn't burn down your margin in token spend. Most teams stall here because LLM features look easy in a demo and hard in production — prompt versioning, evals, caching, rate limits, hallucinations, latency.

AI features we ship

Semantic and hybrid search

Search that understands intent, not just keywords. Built on pgvector, Pinecone, or Qdrant with reranking and hybrid keyword + vector queries. Drop-in replacement for your existing search bar.

In-product copilots

Side-panel assistants that know your product's data and can take actions. Tool-using, with action confirmation for irreversible operations. Designed to live inside your existing UI.

Summarisation and digests

Long-document summarisation, meeting recap generation, daily / weekly digests. Streamed responses, optional citations back to the source.

Classification and extraction

Auto-tag tickets, route leads, extract structured data from unstructured input (emails, PDFs, voice transcripts). With eval suites so you can prove the accuracy stays above your bar over time.

AI-powered onboarding

Personalised first-run flows. The product reads what the user just signed up for and configures itself accordingly. Massive activation lift, small build.

What you get

• A production AI feature integrated into your existing codebase (React / Next.js / Vue / Rails / Django / Node — we work in your stack, not ours).
• Eval suites that catch quality regressions before they ship.
• Prompt versioning with audit trail.
• Cost guardrails — per-user budgets, model routing (cheap for easy, premium for hard), prompt caching, batch APIs.
• Streaming UX with cancel, retry, and copy-to-clipboard.
• Observability — Langfuse / Helicone / OpenTelemetry — so you can see exactly what every prompt is doing.

Tech stack

Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-source models. Vercel AI SDK or LangChain for orchestration. pgvector / Pinecone / Qdrant for retrieval. Streaming via Server-Sent Events or WebSockets. Eval frameworks: PromptFoo, Braintrust, or custom Vitest suites.

Industries we serve

B2B SaaS, vertical SaaS, fintech, healthtech, legaltech, edtech, marketplaces, and e-commerce. Most of our integrations land in products that already have product-market fit and want a modern feature.

How we engage

A 2-week discovery sprint to scope the feature, model the cost per active user, and design the eval criteria. Then a fixed-price build (4–10 weeks typical). Optional managed support after launch covers monitoring, prompt iteration, and cost optimisation.

Frequently asked questions

How much will this cost in inference per user per month?

We model that in the discovery sprint, before you sign anything. With caching, batching, and model routing we typically land in the cents-per-user range for most features. Voice and long-document features are higher.

Will you work in our existing codebase?

Yes — we work in your stack. Next.js, Rails, Django, NestJS, FastAPI, Express, Laravel, .NET. We do not ask you to rewrite anything.

How do you handle hallucinations on user-facing features?

RAG with citations, structured outputs, confidence scoring, output validation, and where appropriate a clear "AI suggestion — review before sending" UX. We design the feature so failure modes are visible and recoverable.

Do you sign DPAs?

Yes. We work under enterprise data-processing agreements with Anthropic, OpenAI, and Google — your data is not used for training. Self-hosted open-source options available for sensitive workloads.