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AI Product Development
AI FEATURESUSERSCAN TRUST
We design and build products with language models at their core, from retrieval-based assistants to document pipelines and agents, for teams that need AI features to be accurate, observable and affordable to run.
AI EngineeringWhat we build
We treat a model as an unreliable component, wrapping it in retrieval, evaluation and guardrails until its answers can be measured and trusted.
AI features that hold up outside the demo
- 01LLM product features
- 02Retrieval pipelines
- 03Evaluation suites
A prototype that impresses in a demo is easy to build. A feature that answers correctly on real data, at acceptable speed and cost, is engineering. This service covers model selection, prompt and context design, retrieval over your documents, tool-using agents, evaluation, guardrails and the application around them, built with Python or TypeScript and models from OpenAI, Anthropic, Google or open source.
It fits when language, documents or unstructured data sit at the center of the task, and when a mostly right answer reviewed by a person is valuable. Where rules or classic software would do the job, we recommend those instead. We begin with a small set of real examples and a way to score outputs, then improve the system against that score.
Talk to us about your projectCapabilities, End to End
What It Looks Like When It Ships
From Kickoff to Launch
We define the task, the user, what a good answer looks like and what failure costs, collect real examples, and check feasibility with a quick spike before committing to a design.
A first pipeline is built with the simplest approach that could work, alongside an evaluation set and scoring method, giving every later change a number to beat.
We improve retrieval, prompts, model choice and tool design in measured steps, reviewing failures with your domain experts, until results meet the acceptance threshold we set with you.
The feature is integrated into your product with guardrails, rate limits, fallbacks and tracing, released to a limited audience first, and monitored for quality, latency, cost and user feedback.
Built for Teams Like Yours
What buyers ask before they start
If yours is not here, ask us directly. The first conversation is free and useful whether or not we work together.
Book a callWe choose by testing, not by reputation. Candidate models from OpenAI, Anthropic, Google and open source are run against your evaluation set and compared on quality, latency, cost, context length and data handling terms. Often a smaller model handles routine requests and a stronger one takes the hard cases. We keep the model behind an abstraction, so you can switch providers as prices and capabilities change.
Start with prompts, since they are the cheapest to change. Add retrieval when the model needs facts it was never trained on, such as your documents, policies or product data, or when answers must cite sources. Consider fine-tuning when you need a consistent style or format, a narrow classification task, or a smaller, faster model at volume. In practice, good prompts combined with retrieval cover most business use cases.
No one can remove them entirely, so we design for them. Answers are grounded in retrieved sources and shown with citations. The model is instructed and tested to say when it does not know. Outputs that feed other systems are validated against schemas and business rules. An evaluation set tracks accuracy across releases, and for high-stakes decisions a person reviews the result before anything is acted on.
Data handling is decided with you before any integration is built. We choose provider offerings whose terms exclude training on customer data, such as Amazon Bedrock or Azure OpenAI, review those terms with you and can select regional hosting. Sensitive fields can be redacted before a request leaves your systems. Where data must not leave your environment, we deploy open models on infrastructure you control.
Cost is designed, not discovered on the invoice. We estimate tokens per request during scoping, then reduce them with tighter context, prompt caching and retrieval that sends only relevant passages. Simple requests go to cheaper models, repeated ones are cached, and offline work is batched. Per-user and per-tenant limits prevent runaway usage, and a dashboard shows spend by feature so pricing decisions rest on real figures.
