AI Division
Copilots and search inside your own product, priced so you know the monthly bill.
Software companies and in house product teams want AI built into the product their users already open. We design and build copilots, search, summaries and MCP connectors that suit your interface, your data model and your cost limits.
In short
What does AI Inside Your Products from Code Colonies cover?
Copilots, smart search and MCP connectors in your apps, ERP or CRM. Deliverables include AI feature roadmap; working features in your codebase; an MCP server where it helps; cost limits for each tenant. The work is delivered under 2good.dev at USD 2 or INR 199 an hour, a 150 hour minimum, no advance and payment after you accept the work.
- How does the work run?
- Assess (1 to 2 weeks), pilot (2 to 3 weeks), production (rollout) and managed operations (monthly).
- Which tools and standards are used?
- Claude, OpenAI, Gemini, MCP, Next.js and React, chosen to fit your systems.
- Who owns the result?
- You do. Once you accept and pay, the documents, code and intellectual property from the work are yours.
- How do I start?
- Book a call or send a short brief. A specialist replies within one working day.
When clients call us
Situations we are asked to resolve
- Customers keep asking when your product will have AI features.
- Users have trouble finding things in your app.
- You want other AI tools to work with your product through MCP.
- Your AI prototype looked good in a demo but struggles with real load.
Scope
What the work covers
- Feature discovery
- We find the points in your product where AI saves users real effort.
- Copilots
- In app assistants that know the current screen, the record and the user's role.
- Smart search
- Semantic and hybrid search across your product data.
- MCP connectors
- Model Context Protocol servers so assistants like Claude can use your product safely.
- Cost and latency
- Caching, model routing and a budget for each tenant.
- Evaluation in CI
- Automatic quality checks run on every release.
How it runs
A clear path, with gates
- Step 11 to 2 weeksAssessReview the product, talk to users and shortlist features.
- Step 22 to 3 weeksPilotShip the first feature to a group of real users.
- Step 3RolloutProductionRelease to everyone, with monitoring and cost limits.
- Step 4MonthlyManaged operationsModel updates, quality tracking and new features.
Deliverables
What you receive, and what changes
What you receive
- AI feature roadmap
- Working features in your codebase
- An MCP server where it helps
- Cost limits for each tenant
- Automated evaluation suite
What changes
- AI features people keep using
- Usage limits that keep the monthly bill predictable
- Safer access for outside AI tools
- Answer quality tested against your own examples
- Typical stack
- ClaudeOpenAIGeminiMCPNext.jsReactNode.jsPythonpgvectorVercel AI SDK
Start here
Talk to us about AI inside your products.
Tell us what you are trying to achieve. A specialist replies within one working day, and the first conversation is free.
Questions
Will you work in our existing codebase?
Yes. Our forward deployed engineers work in your repositories, follow your conventions and go through your code review like anyone else.
What is MCP?
The Model Context Protocol is an open standard for connecting AI assistants to tools and data in a controlled way. With an MCP server, your customers can use your product from their own AI assistant.