AI Engineering
AI accelerates. It does not plug gaps.
If you have gaps in how you ship software or run internally, AI will surface them and make them worse. That is the reason the process work comes first, and it is why most pilots stall between the demo and production.
Built in your environment. You own the code. Fixed price when the scope is clear, a dedicated squad when it isn't.
The bottleneck
Your growth is gated by manual operations.
Your best estimator's knowledge lives in their head. Your pricing logic lives in spreadsheets. Your job history lives in six systems. Every quote, schedule, and invoice waits in a queue for the one person who knows how.
That's not a tooling problem. Buying licenses doesn't fix it. It's an engineering problem: the knowledge and the logic need to become a system your whole team uses, with your experts approving every output until the accuracy data says they don't have to.
AI Quoting Accelerator
- Scope
- One quoting workflow end to end: intake → historical job retrieval → draft quote with your pricing logic → human approval → ERP/CRM write-back
- First step
- An AI Value Assessment in quoting mode: four weeks, from $10,000, ending with a fixed-price proposal for the build
- Success metric
- A time-to-quote target, agreed up front, in writing
Where it goes next: quoting → order entry → invoicing → full quote-to-cash.
See the Quoting Accelerator →The roadmap
Three questions every AI roadmap has to answer.
A roadmap is finished when it answers these three in writing, with names and numbers, and not before.
1. Where is the value?
Which workflows, in what order, and what each one is worth in hours, cycle time, or margin. The answer is a ranked list with a number next to each line.
2. What are the guardrails?
What data the tools may touch, who can approve what, how an agent proves who it is acting for, what it costs per month, and what gets logged. Setting them after the build is how pilots get shut down.
3. Who builds it, and who runs it afterward?
A roadmap that assumes a team you do not have is a wish list. For most mid-market companies the honest answer is a small embedded team now, growing only as the business case justifies it, with your own people trained to own it by the end.
The AI operating lifecycle
The assessment is the first step, not the product.
Every AI engagement sits somewhere on this arc, one engagement per stage. The stages overlap in practice: guardrails are defined during the assessment, and enablement runs alongside the build.
Roadmap
A ranked list of workflows with a value next to each, and a fixed price for the first one.
Guardrails
Data boundaries, approval rules, agent identity, cost controls, logging, a sandbox, and the ability to shut it off. Defined in week two of the assessment, built in the first build.
Build
The integrations and workflows, inside your ERP, CRM, or operations platform rather than next to them.
Run
CI/CD, observability, infrastructure as code, and alerting, so what ships keeps working.
Enable
A team of your own that can own and extend it. Your people are shipping with ours by the time the first workflow is live.
We shut down our pure GRC practice to hire more technical people. We kept the discipline; we stopped selling the paperwork.
Pilot to production
You already started. We get it to production.
Pilot to production is the Guardrails and Run stages applied to AI work you have already started, whether it was built by your own team, by an outside vendor, or by a leader on their own.
Companies with a software team
AI has you shipping far more code, which amplifies every gap already in your SDLC: approval processes, observability, monitoring of what agents are doing, QA, documentation.
Companies without one
You built it. Now what? There is a gap between writing a tool and owning one. You will not roll a tool out without guardrails, and someone has to maintain it after the pilot.
The guardrails bet
Governance is an enabler.
Five years ago we were pushing SDLC guardrails and half our clients said it cost too much. Those who did it are now the fastest on AI.
How it runs
Embed. Ship. Measure.
Embed
Senior engineers join your team — your repos, your stack, your security model.
Ship
Production in weeks, not quarters. Human-in-the-loop until the accuracy data says otherwise.
Measure
Success is a number you already track — cycle time, win rate, hours saved — agreed up front.
AI drafts. Your expert approves.
Accuracy is measured and ratcheted before anything goes autonomous —
and autonomy is something you choose later, not something we impose.
Fair questions
The objections, answered straight.
"Shouldn't we just buy a tool?"
Custom plumbing, not custom creation. We build on the platforms you already run. If licensing is cheaper than building, we will say so.
"Is AI accurate enough for pricing?"
Alone, no. We ship human-in-the-loop first: AI drafts, your expert approves. Accuracy is measured and ratcheted before anything goes autonomous.
"Claude, OpenAI, or Gemini?"
Most problems today are context problems, not model problems. Opinions on tools change every quarter; what we build has to last longer than that.
"A big firm pitched us the same thing."
Ask them who writes the code, what it costs, and when it ships. Then ask us.
What we do not do
- Model training
- Data science research
- Selling a platform
- Staff augmentation
If a roadmap needs a custom model, we say so in the readout and name who should build it.
Anthropic Partner Network member
We run on what we sell. Our own ops are AI-native.
Tell us which workflow is the bottleneck.
Start with an AI Value Assessment: four weeks, from $10,000, and you keep the deliverables.