AI Enablement & Integration

AI Built into Your Product. Not Bolted On.

We embed AI agents, workflows, and assistants directly into your app. LLM integration, AI agents, workflow automation, and fine-tuning built for production — in weeks, not quarters.

6
Core AI use cases we build every day
20+
Products shipped with AI integration
Weeks
From use case to production-grade AI feature
5★
Rated on Clutch, Google & GoodFirms
What we build

Six AI Use Cases We Build Every Day.

Every engagement starts with identifying the right use case. Here are the most common ones we build.

AI Notes & Summaries
Automatic meeting notes, clinical documentation, call summaries, and information capture — built into the workflow, not added on top.
AI Agents & Assistants
Autonomous agents completing tasks on behalf of users — scheduling, triaging, routing, and acting without manual input.
Semantic Search
Intent-based search using vector embeddings and RAG. Users find what they mean, not just what they type.
AI Chat & Copilots
Context-aware interfaces trained on your data. Not a generic chatbot — a product-native assistant that knows your domain.
Workflow Automation
AI-triggered actions and task routing. The right thing happens automatically, at the right time, for the right user.
AI-Powered Analytics
Intelligent dashboards surfacing key insights from your data — without users having to know what to look for.
Who it's for

For Product Teams Ready to Ship AI That Works.

Not "exploring AI." For teams with a clear use case and a product that needs it built properly.

Startups Shipping AI Features
You need an AI feature shipped in weeks, not quarters. We take your use case from scoped to production without the back-and-forth of working with a team that's learning on your dime.
Teams with a Failing AI Build
Your team tried to build it but the results were inconsistent or the quality wasn't there. We come in, assess what exists, and either fix it or rebuild it — properly.
Businesses Falling Behind Competitors
Your competitors are shipping AI features and you're behind. We help you identify the highest-ROI use case and get it live fast — measured by business metrics, not vibe.
How it works

Use Case First. Production Second.

We never start with a model or a technology. We start with the problem you're solving and work backwards to the best AI implementation.

01
Use Case & Scoping
We identify the specific AI use case that will move the needle. Use-case-first roadmap before a line of code is written. Deliverable: scoped proposal + fixed price.
02
Data & Architecture
We assess your data, define the integration architecture, and choose the right model and approach. No over-engineering — the simplest solution that works in production.
03
Build & Evaluate
Production-grade prompts, evals, and fine-tuning. We build with monitoring from day one — so quality is measurable, not assumed.
04
Integration & Handover
Woven into your existing product flow. Full documentation, team training, and handover. No lock-in — you own everything.

Ready to add AI to your product?

Tell us your use case. We'll scope it and tell you exactly what it'll take to ship it — in weeks, not quarters.

FAQs

AI Questions, Answered Honestly.

No hype. Just straight answers from a team that's shipped AI into production products.

It depends on the use case — and we'll tell you honestly. For most product use cases, a well-engineered prompt and RAG pipeline with an existing model (OpenAI, Anthropic, or an open-source alternative) will outperform a custom model at a fraction of the cost. Fine-tuning and custom models make sense in specific scenarios. We recommend what's right for the problem, not what sounds most impressive.
This is the most important question in AI product development. We build evaluation frameworks alongside the feature — testing accuracy, hallucination rate, edge cases, and latency before anything goes live. Production-grade prompts are engineered, not just written. And we monitor outputs in production so you know when quality drifts.
We'll assess it. If it's salvageable with prompt engineering, evals, and architecture changes, we'll fix it. If the approach is fundamentally wrong for the use case, we'll tell you directly and propose a rebuild. We don't patch what can't be patched.
We work with your existing stack wherever possible. Most AI integrations are additive — they call an API or a model endpoint and return structured output. If new infrastructure is needed (vector database, fine-tuning pipeline, etc.), we'll spec it in the scoping phase so there are no surprises.
You own everything — prompt templates, fine-tuned weights, RAG pipelines, evaluation datasets. Full IP transfer on final payment. No lock-in to our infrastructure or our team.