01 / REASONING SYSTEMS
Make the complex legible.
We turn fragmented operational data into a reasoning layer your team can trust — retrieval, evaluation, and guardrails included by default.
AI reasoning · Applied AI systems · Wyoming, USA
Think deeper.
Build smarter.
Reasoning systems operational · Cheyenne, WY
AI Infer means AI reasoning. AI INFER also means the love of it.
We are an applied AI studio. We design, build, and ship reasoning systems that make complex operations legible, decisions faster, and software genuinely worth using.
14
Days to a working prototype
100
Percent senior-led delivery
24
Hour first-response promise
3
Parallel workstreams, one team
What we do
Three things, done properly.
We are deliberately narrow. Reasoning systems, the software that carries them, and the enablement that keeps them alive after we leave.
02 / PRODUCT ENGINEERING
Ship software that earns its keep.
Custom applications, internal tools, and AI-native interfaces built to survive real users, real load, and real scrutiny.
03 / ADOPTION & ENABLEMENT
Make it stick after launch.
Rollout plans, team training, and measurement that proves impact instead of assuming it.
Engagements
Defined scope. Defined price. No discovery theatre.
Every engagement is written down before it starts: what we deliver, what it costs, and how we will know it worked.
A
AI Strategy & Discovery
A short, focused engagement to find where AI actually pays off in your business — and where it quietly does not. You leave with a sequenced roadmap, a cost model, and a clear decision.
- 2–3 weeks
- Opportunity map
- ROI model
- Risk review
B
Custom AI Applications
Production software with reasoning at the core: assistants over your own knowledge, document workflows, decision support, and customer-facing tools that behave predictably under pressure.
- Retrieval
- Agents
- Evaluation
- APIs
C
Workflow Automation
Connect the systems you already run. We remove the manual glue between inboxes, spreadsheets, and line-of-business tools — with human review where it still matters.
- Integrations
- Orchestration
- Human in the loop
D
Data & Evaluation Engineering
The unglamorous work that makes AI reliable: pipelines, retrieval quality, evaluation harnesses, cost control, and monitoring that tells you the truth.
- Pipelines
- Vector search
- Eval harness
- Observability
How we work
Small teams. Short loops. Written work.
We keep our teams small and senior, and we keep the feedback loop short. You talk to the people building the thing, every week.
01
Frame the problem
We start with the decision you need to make and the constraint you cannot move. No solution until the problem is sharp.
02
Build the smallest real thing
A working slice in weeks, not quarters. Real data, real users, real feedback — before the budget gets large.
03
Harden, measure, hand over
Evaluation, monitoring, documentation, and training so your team owns it after we leave.
Selected work
Proof beats promises.
A representative engagement, described honestly. Client details are generalized at their request.
NORTHSTAR / OPERATIONS INTELLIGENCE
“We stopped asking what AI could do and started asking what our team needed to decide.”
Turning a fragmented operation into one confident decision surface.
A growing services business ran on eleven spreadsheets, four shared inboxes, and a weekly meeting that existed mostly to reconcile them. Every answer took a day and arrived with an argument attached.
We replaced the reconciliation with a reasoning layer over their operational data — every answer citing its source record, so nothing had to be taken on faith.
42%faster reporting
3.5×daily adoption
90days, brief to launch
How we are different
Four commitments we put in writing.
These are the terms we work under, not values on a poster.
Senior people only
The people in the first call are the people doing the work. No bait-and-switch staffing.
Fixed scope, fixed price
You approve a defined engagement and a defined number before we begin.
Everything written down
Architecture, prompts, and evaluations are deliverables, not tribal knowledge.
Boring where it counts
Security, cost ceilings, and rollback plans are part of the build, not an upsell.
Questions
The things people ask before they hire us.
How does an engagement usually start?
With a 45-minute working session. We ask what decision you are trying to make faster, what data exists, and what has already failed. You leave with a point of view whether or not you hire us.
Do you work with our existing systems?
Yes. Most of our work sits on top of systems clients already run — cloud platforms, line-of-business tools, data warehouses. We do not require a migration to begin.
How do you handle data privacy and security?
Data stays in your environment or a region you approve. We use least-privilege access, log every model call we build, and document what leaves your boundary. We will sign your NDA and DPA.
What does a realistic timeline look like?
A discovery engagement runs two to three weeks. A first production-grade slice typically runs six to ten weeks, depending on integrations and review cycles.
Start here
Tell us the decision you need to make faster.
Send a note with the problem, the constraint, and the timeline. You will get a real reply from a practitioner — not a sales sequence.
[email protected]
www.aiinfer.top
AI Infer LLC · 312 W 2nd St Suite A8123, Casper, WY 82601
Registered in Wyoming · C Corp filings on record