AI
AI Integration Services for Existing Products
Add practical AI capabilities to existing applications and SaaS products — assistants, document Q&A, RAG, search and automation as production features.
What this covers
- AI assistants and in-product chat
- Document Q&A and RAG systems
- AI search across your own records
- Classification, extraction and summarization
- Workflow automation with model calls and tools
- AI features inside existing web and SaaS products
In short
Webstume integrates practical AI capabilities into existing applications and SaaS products — AI assistants, document Q&A, retrieval-augmented generation (RAG), AI search, classification, data extraction, summarization and workflow automation — as production features rather than isolated experiments. The feature lives inside your application's interface, APIs, storage, permissions and monitoring. If a simpler automation would solve the problem more reliably, we will say so.
Problem
The situation we solve.
AI only helps when it is engineered into a real product. Isolated demos, unowned prompts and features that ignore your data, permissions and workflows create cost without reliable outcomes.
Who this is for
You are probably in one of these situations.
- Products with a repeated user task that AI could genuinely shorten
- Teams sitting on documents or records they cannot search usefully
- Businesses that built an AI demo and need it turned into a production feature
- Companies that want AI inside an existing app rather than bolted alongside it
Deliverables
What you end up with.
- 01An AI feature running inside your product rather than as a separate demo
- 02The retrieval, data and permission design the feature depends on
- 03Monitoring of how the feature behaves and what it costs to run
- 04An application architecture that stays under your control rather than a vendor's
What we need from you
Day one.
- Access to the product (or a staging environment) where the feature will live
- Sample data that represents what the feature will see in production
- A clear success criterion — what a good answer or action looks like
- Any constraints on data handling, providers or cost
Approach
How we approach it.
01
Ground the request in context
Retrieve the relevant documents, records or product state the model needs before anything is generated.
02
Call the model with a defined job
The model call has a bounded task — answer, classify, extract, summarize — not an open-ended chat for its own sake.
03
Use tools where the product must act
When the feature needs to look something up or trigger a workflow, that happens through controlled application tools, not free-form guessing.
04
Return a grounded response
The user sees an answer tied back to your data and product rules, with failure modes and cost visible to the people running the system.
Technologies
Tools that support the work.
- Next.js
- React
- TypeScript
- Node.js
- AI APIs
- RAG
- PostgreSQL
- Vector search
- Redis
Engagement
How working together runs.
AI integration is scoped individually. We prefer to start with one feature that can be judged on real usage before committing to more. Provider running costs depend on volume, so they are discussed openly. Ongoing engineering retainers are available after the first feature ships — scope and capacity are agreed in a proposal.
FAQ
Questions about this service.
What is retrieval-augmented generation (RAG)?
RAG means the application retrieves relevant content from your own data first, then asks the model to answer using that content. It is how you get answers grounded in your documents and records instead of the model's general knowledge.
Can you add AI to my existing app without rebuilding it?
Usually yes. AI features are built into the application you already run — its interface, APIs, storage and permissions — rather than standing alongside it as a separate tool.
Which models or providers do you work with?
We choose providers based on the product's requirements — quality, cost, latency, data handling and operational fit. The application architecture stays under your control rather than being locked to one vendor. Specific providers are agreed for each engagement rather than published as a fixed list.
How do you handle our data?
Data handling follows what the product already requires: access control, what leaves your environment, and what the chosen provider allows. Exact practices are agreed for each engagement based on your stack and constraints — we do not invent a one-size privacy claim here.
How long does a first AI feature take?
It depends on data access, permission design and how clear the success criterion is. Timeline is scoped after a first conversation about the product and the job the feature must do.
How much does AI integration cost?
AI work is scoped individually. Ongoing engineering retainers are available for continued work after the first feature; scope and capacity are agreed in a proposal rather than published as a fixed public price.
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