Software shaped around how you actually work
Agentic systems, internal tools and AI that touches your real data instead of guessing.
From $700/mo
Ongoing maintenance and iteration once the first version is live
Fixed scope
A written specification and a fixed quote before any money changes hands
Your data
Agents that read and write real records, not a chatbot reciting a prompt
When off-the-shelf stops fitting
Most businesses should buy software rather than build it. These are the situations where that stops being true.
You run four tools that do not know about each other
Data is re-keyed between systems by a person, every day. The cost is invisible because it is spread across salaries, and it is usually larger than the software would have been.
Your process is the competitive advantage
You do it differently on purpose, and that is why customers choose you. Generic software forces you back toward the average, which is the opposite of what you want.
You are paying per seat for 10% of a product
Five subscriptions, each used lightly, together costing more per year than a focused internal tool that does only your job properly.
You want AI on your data, not on the internet
A general assistant that has never seen your records can only give general answers. Useful AI here has to read your history, your pricing and your rules.
What we build
Agentic systems
Software that takes a goal and works through the steps: reading records, calling tools, making a decision within rules you set, and escalating to a person when it should. Not a chat window with a prompt behind it.
Document and intake processing
Extract structured data from PDFs, forms, emails and scans, validate it against your rules, and push it into the systems that need it. This is where most manual hours actually go.
Voice and chat agents
Agents that answer, qualify against your real availability and pricing, book, and write the outcome back to your CRM. Connected to your data, so they can answer a question rather than deflect it.
Internal tools and portals
The dashboard, approval queue or client portal that your process needs and no vendor sells. Usually the highest-return thing on this list.
Integration layers
A service that sits between your systems and keeps them honest, with transformation, validation and logging you can read.
Decision support
Scoring, prioritisation and forecasting on your own history, presented where the decision is actually made rather than in a report nobody opens.
How a build runs
- 1
Discovery call, free
Thirty minutes on the actual process, the systems involved, and what the manual version costs you today. If the answer is "buy this existing product instead", we will say so.
- 2
Written specification and fixed quote
What it does, what it explicitly does not do, what we need from you, the timeline and the price. Before any payment, and detailed enough to hold us to.
- 3
A narrow first version
The smallest thing that is genuinely useful, in production, early. Real usage tells you more in a fortnight than another month of planning.
- 4
Iterate on evidence
We watch how it is actually used and build the next piece against that, rather than the feature list everyone imagined at the start.
- 5
Handover or retain
Documented code and infrastructure you own. Take it in-house, or keep us on from $700 a month for maintenance and continued work.
Where AI belongs, and where it does not
This gets more attention than it deserves in most proposals, so here is our actual position.
- Use a language model where the input is genuinely unstructured: free text, documents, speech, messy human phrasing. That is what it is good at.
- Use ordinary deterministic code for anything with a right answer: pricing, tax, eligibility, scheduling maths. A model that is right 97% of the time is a liability there, not a feature.
- Put a person in the loop wherever a mistake is expensive or hard to reverse, and design that handoff deliberately rather than bolting it on later.
- Log what the system decided and why. An agent you cannot audit is an agent you cannot fix, and eventually one you cannot defend.
- Design for the model being wrong, because sometimes it will be. Retries, validation and a fallback path are the difference between a demo and production software.
The stack, briefly
We are not attached to particular tools and pick per project, but for context: TypeScript across the stack, Postgres for relational data, edge deployment for anything user-facing, and whichever model family fits the task and the budget rather than whichever is fashionable. Where a task can be done reliably without a model, we do it without a model, because it is faster, cheaper and easier to test.
Common questions
Something not covered?Email usand a person replies.
What does a custom AI build cost?
Fixed-scope builds are quoted after a discovery call. The range across the work we take on is wide enough that a number here would mislead you, so we would rather give you a real figure against a real specification. Ongoing maintenance and iteration is retained from $700 a month once the first version is live.
How is this different from buying an AI tool?
A product is built for the average of its market, which is exactly the situation you are trying to escape. Custom software is built around how your business actually works, connects to the systems you already run, and does not charge per seat for capability you never touch. It is only the right call when your process is genuinely non-standard, and we will tell you when it is not.
Will the AI have access to our data?
That is the entire point. An agent that cannot read your records can only give generic answers. We connect it to the systems that hold the truth, with scoped permissions, so it can answer specifically. What we do not do is send your data anywhere it does not need to go, and the specification says exactly where it goes.
What if the AI gets something wrong?
It will, occasionally, which is why we design for it. Anything with a definitive right answer is handled in ordinary code rather than by a model. Anything expensive or irreversible gets a human approval step. Everything is logged with its reasoning so a wrong answer can be traced and corrected rather than argued about.
Do we own what you build?
Yes, for fixed-scope engagements: the code, the infrastructure definitions and the documentation. You can take it in-house or to another developer at any point. We retain our own internal libraries, which is standard and is part of why we are quicker than starting from zero each time.
How long does it take?
A focused internal tool or a single agent is typically four to eight weeks to a usable first version. Larger platforms run longer. We aim to get something narrow into production early rather than disappear for a quarter, because real usage reshapes the plan more usefully than more meetings.
What if we already have a GoHighLevel setup?
Then we build around it rather than replacing it. Most of our work connects to GoHighLevel rather than competing with it. If your need is mainly integration and reporting inside the platform, our custom GHL development service is the better fit and usually the cheaper one.
Do you take on small projects?
Sometimes, if the scope is genuinely contained. A single integration or a focused internal tool is a reasonable first engagement. What we avoid is a large, vague brief with no clear first milestone, because those tend to disappoint everyone involved.
Book a free 30-minute discovery call
Describe the process that is costing you hours. You will get an honest read on whether custom software is the right answer, and what it would take.