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AI consulting that leads to a working system

Make AI useful in your business, around the tools and processes you already have. We help define the problem, build the agreed improvements, and test the result with your team.

Request a free workflow review See examples

$2,500 pilotTwo weeksUSD. Production scoped separately.

Is this the right fit?

Build around a recognizable business problem

Choose this when you know where work gets stuck but need help deciding how AI, integrations, and ordinary software should fit together.

AI implementation starts with the decisions and information your team works with today. A document reader, private company assistant, or approval-based agent needs a clear purpose, reliable inputs, and a way for people to handle uncertainty. The consulting and engineering happen together: we map the workflow, select an approach, then build and test the agreed scope.

Understand business documents

Extract useful information from paperwork, check it against your requirements, and prepare exceptions for review.

Find answers in company information

Build private knowledge search over approved material, with supporting passages and clear limits on what the system knows.

Connect AI to operating systems

Use approved APIs, exports, and integrations so information can move between the systems your team relies on.

Worked examples

See the workflow, including the review points

These interactive examples use sample data. They illustrate behavior; project outcomes are shown separately below.

Search approved information

This legal example illustrates a wider pattern: an answer with a supporting passage and a defined access scope.

Private case searchSample data

Ask the case filesSearch limited to this matter

Where was the blue Honda before the delivery?

Source-linked answer

The deposition places a blue Honda on the east side of the lot before the delivery arrived.

Supporting documentDeposition · page 62Read source

BATES 004512 · Sample excerpt

“…a blue Honda was parked along the east side of the lot before the delivery arrived…”

This excerpt does not establish who owned or drove the vehicle. Those details remain unconfirmed.

Evidence you can inspect. Conclusions for your team.

Explore private legal AI

Delivered work

Project experience behind the service

These are reported outcomes from delivered projects. Client names are confidential; outcomes are specific to each project.

Beverage distribution

Automated weekly reporting

Preparation dropped from hours to minutes.

The recurring pull, comparison, and write-up now runs on a schedule.

Read the project details

Healthcare billing

Claims preparation and exception review

A three-day backlog became a same-day routine.

Documents are read and prepared on arrival. Only exceptions reach a person.

Read the project details

Built to be reviewed

Useful assistance. Clear operating rules.

Checks, approval points, and recovery behavior are part of the design.

For your technical team

Depending on the task, a design can combine OCR, structured extraction, retrieval over private information, APIs, webhooks, and deterministic checks. Python, n8n, databases, and language-model services are tools in the existing engineering stack. We choose them around the required behavior, available access, and operating constraints.

Define permitted sources and actions before connecting systems.
Use explicit checks for known business rules, rather than asking AI to guess.
Keep uncertainty, missing information, and approval points visible.
Plan access, logging, monitoring, and recovery for production.

Private company knowledge, with sources

A company assistant should answer from the material it is allowed to use and show supporting evidence. In delivered beverage-distribution work, field representatives searched a 200-page playbook and received answers with source pages. A new implementation still needs its own approved content, access rules, and tests for unsupported answers.

Explore the matter-search example

AI agents with a defined job and approval boundary

An agent is useful when a task needs several steps: read an incoming request, gather context, prepare a next action, and coordinate a handoff. We define what it may do, what needs approval, and when it must stop. The property-management example keeps quotes above an agreed limit with a manager; it is an illustrative workflow, not a delivered-project metric.

See the approval example

A focused place to start

Test a useful build before committing to production

Start with the decisions, documents, or handoffs that consume your team’s attention. The pilot turns agreed priorities into something your team can try.

What we could improve togetherPrepare documents, add private information search, or connect a review step to existing systems. We agree on the most useful combination and realistic examples together.

What we’ll test together

Outputs can be compared with the source and agreed requirements.
Uncertain cases and actions needing approval reach a person.
Your team can test the working build and review its limitations.

We agree on success criteria before building, then document the results, limitations, and recommended next steps. No promised savings or obligation to proceed to production.

AI implementation pilot

Two weeks

$2,500Agreed priorities · fixed priceAll prices are in US dollars (USD).

The pilot can cover related workflows and improvements. We agree on priorities and deliverables together to fit the two-week engagement.

Priority workflows and related improvements agreed together
Normal cases, exceptions, and approval points mapped
Realistic documents, messages, or system inputs
A working pilot tested against real scenarios
Human review where appropriate
Documentation and source-code handover
Production roadmap and quote for the next phase

Request a free workflow reviewProduction deployment, extensive integrations, security or compliance hardening, and ongoing support are scoped separately.

After the pilot

Production and support have their own scope

Production hosting, monitoring, incident response, maintenance, and ongoing improvements are agreed and quoted separately. The pilot includes source code, documentation, and recommended next steps.

For businesses in the US, Canada, the UK, Europe, and Australia. We agree on time-zone overlap, hosting, access, and data requirements before work begins.

See delivery, ownership, and support

FAQ

Before we get started

What is the difference between AI consulting and implementation?

Consulting defines the problem, constraints, and approach. Implementation builds the system. Our paid pilot combines practical design with a working build, testing, documentation, and source-code handover.

Can AI integrate with the software we already use?

We first check available APIs, exports, permissions, and contractual limits. Integration is planned around access that actually exists. Extensive integrations and production rollout are scoped separately.

Do we need an internal AI team?

No. We work with business owners and existing technical teams. You bring the process knowledge; we turn the agreed priorities into a build your team can evaluate.

How much does AI implementation cost?

The entry pilot is $2,500 USD for two weeks. Priorities and deliverables are agreed together. Production deployment, extensive integrations, security hardening, and ongoing support are quoted separately.

Get started

Request a free workflow review

Tell us what is stuck. We will assess whether it is practical to solve and explain the most sensible next step.

Detailed workflow mapping, technical design, and implementation begin with a paid engagement.

✓A person reads every inquiry
✓We agree on data handling before we access any systems
✓Or email [email protected]