SERVICE — AI
AI that solves a named problem.
Custom AI development for products that already have users
The useful question is never whether to add AI. It is which specific thing your team does by hand that a model can do well enough, often enough, to matter. We work as an AI development company for teams who have that answer, or who want help finding it before committing a budget.
Part of Build and Transform — every engagement starts with an audit
We start from the manual process, not the model
Every AI project we have shipped replaced something a person was doing by hand, on a schedule, at a cost someone could name.
The projects that last do not start with a model choice. They start with a bottleneck someone can describe: a judgement being repeated often enough that the pattern is learnable, and a cost of being slightly wrong that is recoverable. If you cannot describe the manual process the AI would replace, resolve that before anyone writes code — an unclear brief is the most expensive thing on any AI project.
What we build
AI features inside an existing product
Adding intelligence to software that already has users and constraints. This is most of what we do, and it is a different discipline from greenfield work: the feature has to fit an existing data model, release process, and support burden.
Image and document understanding
Comparing what a camera sees against a reference standard, extracting structured fields from unstructured paperwork, flagging anomalies with a confidence score. Each of these is worth building only where the manual version is already a measured bottleneck rather than an inconvenience.
Conversational interfaces
Chat and voice front ends for processes that currently run over email and phone tag. Worth doing when the qualifying questions are consistent and the follow-up is what costs your team time.
Automating manual review
Work where a person reads something and makes a routine call: triaging tickets, checking submissions against rules, sorting inbound into buckets. High volume and low individual stakes is the profile that pays off fastest.
The plumbing around the model
Most of the effort in a working AI feature is not the model. It is getting clean data to it, handling calls that fail, storing outputs somewhere queryable, and giving your team a way to correct it. We build that part properly because it is what breaks.
Cost and latency work
Choosing a model that matches the job instead of the biggest available, caching what repeats, batching what can wait. Inference costs compound quietly at volume, and the cheapest model that clears your quality bar usually sits several tiers below the default.
How we work on this
001
Define what good enough looks like
Before anything is built we agree what accuracy the system has to hit to be worth running, and what happens on the cases it misses. A feature that is right 92% of the time is excellent for triage and unacceptable for billing. That threshold shapes everything after it.
002
Prove it on your data before building around it
We test the core capability against your real material early, while direction is cheap to change. Sometimes the answer is that a model is the wrong tool and a rules engine does the job for a fraction of the cost. Better to find that in week two.
003
Build the boring parts properly
Retries, logging, a correction path for your team, and a record of what the system decided and why. This determines whether the feature is still running in a year, and it is the work most often skipped.
FAQ
Questions we get asked about this.
Do we need our own data to build something useful?
Less than people expect. Modern models bring a lot of general capability, so you usually need examples to evaluate against, not a training set to learn from. A few hundred real cases with known correct answers is enough to tell whether it works, and that is the question blocking your decision.
How much does AI development cost?
It depends on how many of your systems it touches, which drives the number more than the AI does. One well-scoped feature inside an existing product is a different order of magnitude from a system reading from three tools and writing back to two. We scope in phases and give you a number before you commit.
Can you add AI to a product we already have?
Yes, and this is the more common case. We work within your stack, release process, and constraints instead of proposing a rebuild. The first conversation is usually about where the data lives and what your deployment path looks like, because those decide what is feasible.
Who owns the code, the prompts, and the outputs?
You do. All of it, including prompts and evaluation sets, which are real assets and not something we hold back as leverage. We document the reasoning so another team could pick it up without us.
How do you handle sensitive or confidential data?
We scope it at the start, because it constrains the architecture. It is not a setting applied at the end. Depending on your requirements that can mean redacting before anything leaves your systems, using providers with no-training guarantees, or running smaller models in your own environment. If your compliance position rules out an approach, better to know in week one.
Other services
Start with the audit
Tell us how your operation runs today and where it hurts. We will tell you what the audit would cover and what you would have at the end of it — before you commit to anything.
Start with the audit Fixed scope, fixed price — you keep the report either way