Generative AI Applications
Create useful AI experiences grounded in your data, workflow, and product constraints.
A generative AI feature is easy to demo and hard to ship — the gap between "the model produced a good answer once" and "this is reliable enough for a real user" is where most of these projects actually live.
We stay close to your operating reality — the constraints, the edge cases, and the people who have to run the system after launch — so the work holds up long after the first release.
Signals it's time to bring us in
- You want an AI feature grounded in your own data or documents, not a
- Users need to interact with unstructured content — documents, images,
- A copilot or assistant could meaningfully speed up a task your users
- You need to know a feature is reliable enough to ship, not just
Capabilities, built to operate in the real world
Rag Systems
Retrieval-augmented generation grounded in your own documents or data, so answers cite real source material instead of the model's general training.
Multimodal Experiences
Interfaces that let users work with images, documents, or audio alongside text, built around the specific format your content actually takes.
Copilots
An in-product assistant scoped to one real task — drafting, summarizing, searching — rather than a general chat box bolted onto the side of your app.
Content Generation
Generation pipelines with review and edit steps built in, since generated content usually needs a human pass before it ships.
Model Evaluation
An evaluation set built from real examples, so you can measure quality before launch and catch regressions when you change the prompt or model.
Substance over slideware, from first call to production
This is the part most vendors skip. We make the trade-offs visible, keep the team who scoped the work close to the build, and hand over something your business can actually own.
One accountable team
Product, design, and engineering decisions stay under one roof — no hand-offs that lose the plot.
Visible increments
You see working software on a steady cadence, not status theatre or surprise reveals.
Built to be owned
Documented architecture, clean handover, and code your own team can extend confidently.
Risk raised early
We surface the expensive unknowns up front instead of discovering them at launch.
How the product comes together, step by step
A closer look at what we ship
A spread of the surfaces we design and build for engagements like this — from the primary workspace to mobile and reporting.
Dashboard overview
Mobile experience
Detail & records
Insights & analytics
A path from uncertainty to shipped
- 01
Define what "good" looks like for this feature in concrete, testable terms before writing any prompts.
- 02
Build a small evaluation set from real examples to measure against.
- 03
Prototype fast, test against the eval set, and iterate on the prompt, retrieval, or model choice based on actual scores.
- 04
Add the review or edit step a real user needs before trusting the output.
- 05
Launch with monitoring on output quality, then expand once it's proven on real usage.
- A defined, testable quality bar for the feature.
- A working prototype validated against real examples, not a one-off
- The evaluation set itself, so you can keep measuring quality after
- Production implementation with review steps where the output needs
- Documentation covering prompt design and model choice decisions.
- A recommendation for what to test or expand next.
We've watched enough AI demos fail to convince us that "it worked once" isn't a standard worth shipping. We build an evaluation set before we build the feature, so quality is something we can measure and defend, not just something that looked good in a screen recording.
Common questions
We build a small set of real, representative examples and score the feature against them before it ships — the same practice you'd expect from any feature with a measurable quality bar, applied to AI output.
We design the integration so the model is swappable — the evaluation set lets you re-score a new model quickly and see whether it's actually an improvement before committing to the switch.
separate? Almost always on top of what you have. The goal is usually one well-scoped feature inside your existing product, not a parallel AI app your users have to learn separately.
A system, not a set of disconnected parts
We design the whole pipeline — from where data originates to where your team takes action — so nothing important lives in a spreadsheet or someone's head.
Ship AI features that are measured, not just demoed.
Tell us what's slow, broken, unclear, or strategically important. We'll help turn it into a sensible plan.