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Generative AI that survives past the demo
Uptut builds GenAI applications grounded in your own data: assistants, document intelligence, semantic search and AI features inside products you already ship. Every build carries an evaluation harness, cost controls and guardrails from the first sprint, because those are what separate a working prototype from something you can put in front of customers.
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What makes a GenAI build production ready
Answers grounded in your documents and citable back to source, rather than plausible and unverifiable.
An evaluation harness built in the first sprint, so you can tell when a change made quality worse.
Cost and latency engineered per feature, with model routing that keeps the bill flat as usage grows.
Shipped into your existing product and auth stack, not delivered as a standalone app nobody adopts.
Your GenAI build roadmap
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Experience the Uptut Edge
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Each project personalised for your business
Unique processes: each service customised to serve your projectChoose one-time projects or ongoing managed consulting
- Global support: work with consultants in your timezones
Outshine competition with expertise and technology
- Expert consultants with average 10 years of experience across major industries
Best industry practices and proven frameworks for your domain
Next-gen technology including AI/ML and state-of-the-art tooling
Get more than just a generic report
Proactive discovery workshops to surface hidden risks and opportunities
Multi-domain expertise covering cloud, platforms, applications and enterprise systems
Summary reporting for key stakeholders and detailed assessments for your technical team
Frequently Asked Questions
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Anything where a language model does real work inside a product or workflow: an assistant answering from your knowledge base, a system extracting structured data from contracts or invoices, semantic search across internal content, drafting and summarisation tools, or an AI feature inside software you already ship. It is applied engineering against your data, not a model licence and not a chatbot wrapper.
Buy when the workflow is generic and the vendor already covers it. Build when the value depends on your data, your process or your product surface, which is exactly where off-the-shelf tools plateau. Most clients do both. We will tell you at scoping if what you have described is already solved by a product you can license for less than the build would cost.
A working prototype with an evaluation baseline typically takes four to six weeks. Production hardening, integration and rollout adds another eight to twelve. A contained internal tool can ship in six to eight weeks end to end. Anything involving new data pipelines or unresolved permissions runs longer, and we scope that honestly rather than discovering it in month three.
You own the codebase, the prompts, the evaluation datasets and the infrastructure definitions. We build behind a model abstraction layer so the underlying provider can be swapped without a rewrite, and we document what would need retesting if you did. Nothing in the handoff depends on Uptut tooling or a continuing contract.
Yes, and it is a common brief. We assess what exists against production requirements, then sort it into keep, rework, replace and build new. Prototypes usually hold up on the core logic and fall down on retrieval quality, evaluation, cost at volume and access control. You pay for the gap rather than a rebuild.
Against an evaluation set built during the engagement, not against impressions. We agree the quality bar up front, hold out a test set your team helps label, and track accuracy, grounding, refusal behaviour, latency and cost per transaction on every change. The production gate is a number you approved, not a demo that went well.
Get a GenAI build that reaches production
Book a free scoping call with Uptut experts.
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