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AI Consulting

AI Consulting from Engineers Who Build the Systems

Most organizations do not lack AI ideas. They lack a clear view of which ones are feasible, worth the investment and safe to run. Our consulting work answers those questions with evidence. We audit your processes and data, rank opportunities, test the leading candidate with a proof of concept on your real data, and give you a roadmap your team can act on.

Practitioner advice

Recommendations come from engineers who design, build and operate AI systems in production.

Evidence over opinion

Key assumptions are tested on your own data before they shape the roadmap.

Vendor neutral

We compare hosted, open-weight and off-the-shelf options on your requirements, not on a sales relationship.

What you get

AI Consulting: what is included

AI Opportunity Audit

Structured interviews and process reviews across your departments produce a list of candidate use cases, each assessed for value, feasibility, data readiness and risk.

Data Readiness Review

We examine where your knowledge and records live, their quality, access controls and sensitivity, and identify what must be fixed before AI can use them.

Prioritized Roadmap

A sequenced plan that starts with achievable, high-value projects, states dependencies and required resources, and defines how success will be measured for each.

Proof of Concept

A working prototype of the leading use case, built on a sample of your real data and scored against agreed criteria so the build decision rests on results.

Vendor and Model Selection

Side-by-side evaluation of model providers, open-weight models and packaged products against your quality, cost, privacy and integration requirements.

Governance and Usage Policy

Practical guidance on acceptable use, data handling, human review, vendor terms and accountability, written so employees can follow it.

What AI consulting should deliver for your business

Useful advice ends in decisions, not slides. An engagement should leave you knowing which use cases to pursue first, which to leave alone, what each requires in data, integration and people, and what could go wrong. We arrive at that by looking closely at how work is actually done. We sit with the teams involved, review sample documents and tickets, and inspect the systems an AI solution would need to read from and write to.

Each candidate is then scored on the same dimensions: the effort it removes or the revenue it supports, technical feasibility with current models, the state of the underlying data, the cost of an error, and how easily it fits existing systems. Some ideas turn out to need ordinary automation rather than AI, and we say so. The result is a short, ranked list with reasoning your leadership team can examine and challenge.

  • Ranked use cases with clear reasoning
  • Data and integration gaps identified
  • Risk assessment for each candidate
  • Build, buy or wait recommendations
  • Success measures agreed in advance

From AI strategy to a tested proof of concept

A strategy that has never touched real data is a guess. For the leading use case we build a proof of concept that does. We gather representative inputs, define with your experts what a correct output looks like, and assemble a small evaluation set. The prototype is measured against it, including the difficult cases. You see where the approach works, where it fails, and what it would take to close the gap in a production build.

The same evaluation informs vendor and model selection. We run hosted models from providers such as OpenAI and Anthropic and open-weight models such as Llama through identical tests, and compare quality, latency, running cost and data terms. If your data cannot leave your environment, private hosting is costed alongside the alternatives. You receive a written recommendation, the test results behind it, and an implementation plan you can take forward with us or with your own team.

How we work

A clear process from first call to launch

  1. 01

    Discover

    We interview stakeholders, observe key processes and review your systems, data sources and existing AI usage.

  2. 02

    Assess

    We score each opportunity on value, feasibility, data readiness and risk, and agree the priorities with your leadership.

  3. 03

    Prove

    We build a proof of concept for the leading use case and measure it against criteria agreed beforehand.

  4. 04

    Plan

    We deliver a roadmap with architecture, model recommendations, resourcing and governance for the projects you choose to pursue.

Use cases

Where AI Consulting delivers value

First AI Initiative

Identify the right starting project and avoid investing in a use case that the technology or your data cannot support.

Stalled Pilot Review

Diagnose why an existing AI pilot is not reaching production and define the changes needed to get it there.

Build or Buy Decision

Compare packaged AI products with a custom build against your requirements, integration needs and long-term running cost.

AI Policy and Governance

Establish clear rules for how staff use AI tools with company and customer data, before problems arise.

FAQ

AI Consulting — frequently asked questions

What does an AI consultant do?

An AI consultant helps you decide where artificial intelligence will produce real value and how to implement it responsibly. That includes reviewing your processes and data, ranking use cases, testing feasibility, selecting models and vendors, estimating effort and running cost, and setting out a roadmap and governance rules your organization can follow.

How do we know which AI use cases are worth pursuing?

By scoring them consistently. We assess each candidate for the effort or revenue at stake, technical feasibility, data readiness, the consequence of errors and integration effort. Strong candidates are high in volume, tolerant of human review and supported by accessible data. The top candidate is then tested with a proof of concept before major investment.

What is an AI proof of concept and do we need one?

A proof of concept is a small working prototype built on your real data to test whether an approach meets a defined quality bar. It is worth doing whenever feasibility is uncertain, which is common with language models. It replaces assumptions with measured results and shows what a production version would require.

Should we build a custom AI solution or buy an existing product?

Buy when a product already fits your process, integrates with your systems and meets your data requirements. Build when the workflow is specific to you, needs deep integration, or involves data that cannot go to a vendor. We evaluate both paths against the same criteria and recommend the one that fits, including a mix of the two.

Do we need to prepare our data before starting with AI?

Not always. Many language model applications work with documents and records as they are, provided they can be accessed and are reasonably current. The data readiness review identifies what is usable today, what needs cleanup or better access controls, and which projects can begin while longer data work continues.

Tell us what you want to build

Share your goals and we will reply with a clear plan, timeline and quote — usually within one business day.

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