Generative AI Application Development
Generative AI can draft, summarize, extract, and generate — but a demo that works once is not an application. A production generative-AI feature has to be grounded in your data, safe against bad output, and integrated where people actually work.
We build generative-AI applications that hold up in production: grounded in your content, guarded against hallucination, and wired into the workflow so they save real time instead of creating a new thing to check.
Problems we solve
Impressive demos, unusable products
A generative feature that works in a demo but hallucinates or misbehaves in production erodes trust fast. Real applications need grounding and guardrails.
Ungrounded output
A model answering from its training data instead of your facts produces plausible, wrong content. It has to be grounded in your real information.
Bolted on, not integrated
A generative feature off to the side that no one uses adds nothing. Value comes from being in the workflow.
How we approach it
Grounded in your data
We ground generation in your documents and systems (often via retrieval) so output is based on your facts, with sources — our "no source, no claim" rule.
Guarded and reviewable
Validation, confidence signals, and human review where it matters, so generated content is checked before it is trusted or sent.
Embedded in the workflow
The generative capability lives inside the tools people already use — drafting a reply, summarizing a document, populating a field — so it saves time in context.
What you get
- A production generative-AI feature grounded in your data
- Retrieval/grounding so output cites real sources
- Guardrails: validation, confidence, and human review
- Integration into your existing workflow and tools
- Prompt and evaluation setup for quality
- Monitoring and iteration support
Technologies & integrations
Our delivery process
- 01Frame
Pick a task where generation clearly saves time.
- 02Ground
Connect the model to your data via retrieval.
- 03Guard
Add validation, confidence, and review.
- 04Integrate
Embed the feature in the real workflow.
- 05Evaluate
Measure quality and iterate.
Frequently asked questions
How do you stop generative AI from making things up?
We ground it in your actual data using retrieval so answers cite real sources, add validation and confidence signals, and keep human review for anything consequential.
Which models do you use?
We are model-flexible and choose based on the task, cost, and privacy needs, and design so you are not locked to a single provider.
Can it use our private documents safely?
Yes — retrieval-augmented generation lets the model use your private content at answer time with access controls, without exposing it in training.