01
New projects
A paragraph about the problem is enough to start. We reply within two working days, usually with questions rather than a quote.
AI · Service AI integration into existing software
Add AI to the software you already run — CRM, ERP, support desk or your own product — inside its permissions, data boundaries and release process.
In short
AI integration adds capabilities such as search, summarising, drafting, classification or agents to software you already have, instead of introducing a separate AI tool. The work is mostly engineering around the model: connecting to your data with the right permissions, fitting into your interface and release process, and measuring that the feature works and stays within budget.
- How to engage
- Dedicated engineering pod, Product build
- Practice
- AI
Signs this is the right service
- Your product needs AI features to keep up with competitors.
- Staff switch between your systems and a chatbot, copying data back and forth.
- An AI vendor's tool cannot reach your data or respect your permissions.
- You want AI in internal systems such as CRM, ERP or a support desk.
What you get
Feature selection
The AI features your users will actually use, chosen from how they work today.
Data access
Retrieval and tool access that respect your existing users, roles and tenants.
In-product experience
AI built into your interface, with clear sources, undo and feedback.
Provider abstraction
Models behind an interface, so you can switch providers without rewriting features.
Evaluation and cost control
Tests on real inputs, usage limits per customer, and cost tracking per feature.
The process
- 01
Find the friction
Where users spend time that AI could save inside your product.
- 02
Prototype on real data
A working feature on a staging copy, judged by real users.
- 03
Productionise
Permissions, evaluation, monitoring and cost limits added before launch.
- 04
Release behind a flag
Rolled out to a share of users, measured, then widened.
ClaudeOpenAIpgvectorMCPTypeScriptPythonFeature flags
AI integration: common questions
Can you add AI to a product built by another team?
- Yes. We work in your codebase and follow your review and release process, as part of your team.
Which AI provider should we use?
- It depends on the task, cost, data location and your contracts. We keep providers behind an interface and choose with evaluation on your data.
How do we control AI costs per customer?
- With per-customer usage limits, caching where it is safe, smaller models for simple steps, and cost tracking per feature.
What about data privacy?
- Data stays within the permissions it already has, is sent only to providers you approve under no-training terms, and can stay in your cloud region.
06 / Let’s build
Bring us the difficult problem.
02
Careers
Engineers, designers and researchers who like systems that are hard to get right. Send something you built and what you would change about it now.
03
Studio
Based in India and working remotely, with overlap hours agreed up front so your team always has a time to reach us.
04
Read
Guides on AI agents, RAG, MCP, tokenization and chain abstraction, written by the engineers who build them.