Services
From modernizing a system that already runs your business to building something that doesn't exist yet — scoped, built and handed over directly, with no bench between you and the work.
Short answer
An AI strategy is a ranked shortlist of specific tasks where AI or automation would move a measurable number — each with the data it depends on, the accuracy it has to hit, the person who reviews uncertain output, and a rough cost. It is a sequencing decision, not a technology selection.
In practice that means four steps: list the tasks that consume the most hours or cause the most errors, check which of them have data you can actually reach, build the one with the best hours-saved-to-effort ratio end to end including the human review path, then use what you learn to choose the second. Most engagements here run two to eight weeks per step, and if a task needs a correct answer every time, deterministic software is the right answer rather than a model.
Legacy systems moved, decomposed or re-platformed without stopping the business that runs on them.
See how these engagements runGenerative AI features that survive contact with real users, and automation that removes work rather than relocating it.
See how these engagements runThe connective work that lets a mid-market supplier sell into large enterprises — most of it poorly documented and rarely done well.
See how these engagements runApplications built around how your business actually operates, rather than a product you bend your process to fit.
See how these engagements runNot a build
Sometimes the useful thing is an outside read before budget gets committed — a technical debt assessment, a second opinion on a proposed architecture, or an evaluation of a vendor's proposal by someone with no stake in you buying it. This is usually a few days rather than a few weeks, and it occasionally ends with me recommending you don't build the thing at all.
How every engagement runs
Same shape whether it's a three-week automation or a fourteen-week build. Each phase has a defined output you can hold me to.
A working session on your process — where time goes, where errors creep in, what a fix is worth. You leave with a written opportunity map whether or not we go further.
One workflow, one measurable outcome, fixed price. Deliberately small — a scope you can judge in weeks beats a roadmap you have to take on faith.
Weekly working software, not status decks. You see the real thing in your own environment and redirect while it's still cheap to redirect.
Documentation, a walkthrough with your team, and source in your repository. You are never locked into me — that constraint keeps the work honest.
What a project costs depends mostly on how many systems have to talk to each other and what state your data is in. Indicative starting points are on the pricing page; a fixed price follows the mapping session.
The tools I build with
.NET
C#
Angular
React
TypeScript
Python
SQL
Azure
Azure OpenAI
Generative AI
Agentic AI
Node.js
FastAPI
REST API
DevOps
LangChain
n8n
GitHubCommon questions
An AI strategy is a ranked shortlist of specific tasks in your business where AI or automation would change a measurable number, each with the data it depends on, the accuracy it must hit, the person who reviews uncertain output, and a rough cost. It is a sequencing decision, not a technology selection — the model matters far less than picking the right first task.
Start from the work, not the tools. List the tasks that consume the most hours or cause the most errors, check which of them have accessible data, pick the one with the highest hours-saved-to-effort ratio, and build that single thing end to end including the human review path. Then use what you learn to choose the second. A one-page strategy built this way outperforms a twelve-month roadmap written before anything shipped.
Together, and in that order of priority. The strategy identifies which data actually matters; without it, data cleanup becomes an open-ended project with no finish line. Scope the first use case, then fix only the data that use case depends on.
For a well-scoped single task with accessible data, typically two to eight weeks: about a week to map the process and confirm data access, then the build, then a supervised period where a human checks output before it is trusted. Projects that take longer usually turned out to be several tasks bundled under one name.
A mapping and assessment engagement is priced separately from the build and ends with a written recommendation, a named first project and a fixed price for it. Indicative starting points for each engagement model are on the pricing page.
If the task requires a correct answer every time — statutory calculations, payments, compliance thresholds — deterministic software is the right tool and AI adds risk without benefit. AI earns its place where the input is unstructured or ambiguous, such as documents, email, tickets or free-text records, and where being right most of the time with human review on the rest is genuinely acceptable.
That's what the first conversation is for. It's free, and you'll leave with a written view of the opportunity either way.
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