Build a product
Add AI features to the product you already run
We build AI-powered search, summaries, recommendations and assistants into software you already run, with permissions, evaluation and cost controls around each.
In short
AI features in your product means adding capabilities such as intelligent search, summarization or a conversational interface to software you already operate. Requests pass through an AI gateway that applies permissions, tenant isolation and limits, and each feature is evaluated for quality, latency and cost before release.
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01 The problem
What problem does this solve?
A product team may want smarter search, summaries or an assistant inside the product. Adding one is more than calling a model. It has to respect each customer's data, behave predictably, and not make the product slower or costlier to run.
Without testing, it is hard to know whether a new feature is good enough to ship, or whether the next change makes it worse. What each request costs also needs to be understood before the feature is offered widely.
02 What we build
What do we build?
- AI features designed into your existing user experience
- An AI gateway for model routing, limits and request validation
- Retrieval from your own data where a feature needs context
- Tenant isolation and permission checks on every request
- Versioned prompts and configuration
- Quality regression tests and evaluation before each release
- Monitoring of latency, usage and cost per task
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
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Choose the feature worth building
We prioritize candidate features by user value and feasibility, and we say so if conventional software or automation would serve the need more reliably.
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Prove it on a bounded pilot
A time-boxed pilot tests the feature on representative cases, with success criteria agreed in advance. It ends in a decision to proceed, revise or stop.
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Build it behind a gateway
We integrate the feature through an AI gateway with permissions, tenant isolation and request validation, so model access is controlled in one place.
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Evaluate before release
We test outputs against representative cases and failure scenarios, and define acceptance thresholds before deployment.
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Release and keep measuring
After release we monitor quality, latency and cost per task, and evaluate again as models, prompts and data change.
05 Capabilities and stack
Which capabilities and technologies are involved?
Capabilities
- Python
- FastAPI
- LangChain
- LangGraph
- React
- TypeScript
- Amazon Bedrock
- Google Vertex AI
- PostgreSQL with pgvector
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
A software company wants to offer summarization or intelligent search inside its product, but each customer must see only their own data, and the cost of every request has to stay visible.
We start with a bounded pilot on one feature, build it behind a gateway that enforces tenant isolation, and evaluate it against agreed test cases before it reaches customers.
07 Questions
Questions people ask
No. AI could help in some places, but conventional automation may be more appropriate. We check whether a feature needs AI at all, and we say so if a simpler approach would be more reliable.
Identity, permissions and tenant isolation are enforced before retrieval and before any request reaches the model, and unauthorized disclosure is part of the evaluation. The details depend on how your product stores its data.
The gateway applies rate limits and usage monitoring, and we track latency and cost per successful task from the first release. Those become the baselines to compare against before any benefit is claimed.
09 Terms
Which terms come up here?
If this sounds like you: Take your AI pilot into production, with controls in place