What we do
We turn promising AI ideas into dependable tools that fit your data, workflows and security requirements.
The service
Useful AI starts with a specific business decision or workflow, not a model demo. Before any code is written, we map where a large language model or classifier would change an outcome that currently depends on a person's judgment — a support reply, a document classification, a pricing check — and we size the cost of getting that decision wrong. That scoping work determines whether the right answer is a fine-tuned model, a retrieval pipeline, a simple rules engine, or no AI at all.
Most of the engineering effort sits in the parts a demo never shows: cleaning and chunking source documents, choosing an embedding strategy that matches how your content is actually structured, and testing retrieval against real questions rather than curated ones. We evaluate providers and open models side by side on your own data, not published benchmarks, because the cheapest or most capable model in general use is rarely the best fit for a narrow domain.
Production systems need orchestration logic that decides when to call a model, when to hand off to a person, and when to refuse. We build evaluation harnesses that score accuracy, latency and cost on every change, add guardrails against prompt injection and hallucinated citations, and instrument every call so you can see what the system answered and why it chose that path.
Deployment depends on your data sensitivity and existing stack: a hosted API for lower-risk use cases, or private and self-hosted models where data cannot leave your environment. Either way, the system ships wired into the tools your team already uses — a CRM, a ticketing queue, an internal dashboard — so the output lands where work actually happens instead of a separate chat window nobody opens.
Capabilities
Designed for outcomes
What you receive
Technology
We stay technology-flexible and select the stack around your existing environment, security constraints, team capability and long-term cost.
Frequently asked
A feasibility brief and proof of concept usually takes 3-4 weeks; moving that into a production system with guardrails and monitoring adds another 6-10 weeks depending on integration complexity and how much of your data needs cleaning first.
Most business problems are solved with retrieval and well-structured prompts rather than fine-tuning, which is slower to iterate and harder to govern. We reach for fine-tuning only when a narrow, high-volume task needs consistent formatting or tone that prompting can't reliably hold.
We build an evaluation set from real historical queries before writing production code, then score every model or prompt change against it for accuracy, cost and latency. That same harness runs in production so drift or degradation shows up before users notice.
Yes. Where data residency or confidentiality rules out external APIs, we deploy open-weight models in your own cloud environment or on-premise, trading some raw capability for full control over where data goes.
We don't take on general data engineering unrelated to the AI use case, ongoing content writing, or model training from scratch — those sit with specialist teams or your own staff, though we'll flag clearly when a project needs them before starting.
How we work
Identify a measurable use case
Prototype with representative data
Evaluate quality, risk and cost
Integrate, launch and improve
Have a challenge in mind?
