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AI consulting and integration planning
Find the Right AI Opportunity Before You Build
A practical assessment for small and midsize businesses: we review your processes, tools and data, identify where AI could help and where it would not, and set out a scoped first project with risks, prerequisites and operating-cost factors stated plainly.
Why assess first
Most AI projects fail before the first line of code
They fail because the problem was vague, the data was not available, nobody owned the result or the cost of running the system was never estimated. An assessment answers those questions while changing course is still cheap.
We do not sell a particular AI product, so a recommendation to start small, to fix data first or not to use AI for a task is a normal outcome.
What we hear
Questions we hear before an assessment
“Everyone says we should use AI. Where do we start?”
Leadership interest is real, but no specific process or owner has been identified.
“A vendor demo looked impressive. Will it work for us?”
Demos use clean data and ideal cases; your processes and systems may differ.
“Is our data good enough?”
Information is spread across tools with inconsistent formats and unclear ownership.
“What will it cost to run?”
Build estimates exist, but model usage, maintenance and review time do not.
Scope and deliverables
Proposed deliverables
What a typical assessment would produce. The exact set is agreed in writing for each engagement; these are proposed outputs, not examples of past work.
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Discovery workshop
A structured session with the people who run the processes, to understand the work as it really happens.
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Workflow map
The current process for each candidate area, including systems, hand-offs and exceptions.
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Data and system readiness review
What data exists, its quality, who owns it and how systems can be accessed.
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Prioritized opportunity list
Candidates ranked by value, feasibility and risk, including the ones to avoid and why.
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Risk and governance notes
Privacy, oversight, accuracy and accountability considerations for each candidate.
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Architecture recommendation
A suggested approach for the top candidate: automation, integration, agent or application.
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Pilot scope and test plan
What a first project would include, how success would be measured and what it would need from you.
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Operating-cost factors
The drivers of ongoing cost: model usage, hosting, maintenance and human review time.
Pursue first
Classify inbound inquiries
Clear categories, good examples, low risk.
Higher value · easier
Summarize account history before calls
Data already in the CRM.
Higher value · easier
Worth planning
Extract data from supplier contracts
Valuable, but needs sample review and legal sign-off.
Higher value · harder
Low priority
Draft social media captions
Possible, but little operational benefit.
Lower value · easier
Rethink the scope
Chatbot for every department
Broad scope, unclear ownership.
Lower value · harder
Avoid for now
Automated credit decisions
Regulated, high impact, needs specialist review.
Higher risk · avoid for now
Engagement approach
How an assessment runs
Assessments are fixed-scope engagements, quoted after an initial conversation.
Discovery workshop
We meet the people who run the processes to understand the work as it really happens.
Readiness review
We look at the systems, data quality, ownership and constraints involved.
Opportunity ranking
Candidates are ranked by value, feasibility and risk, including those to avoid.
Recommendation
We propose an architecture and a pilot scope for the strongest candidate.
Written report
You receive the findings, risks, cost factors and a scoped proposal.
Fit
Who it is for
A good fit when you have
- Leadership interest in AI with no settled plan
- Willingness to share how processes really run
Probably not a fit when
- You already have a well-defined project and need it built: go straight to delivery
- You are looking for a document that endorses a decision already made
- No one can make time to explain how the processes work
Trust, security and oversight
How we keep advice useful
Vendor-neutral
We recommend tools and providers on merit, including ones we do not work with.
Grounded in your processes
Recommendations come from interviews and system reviews, not generic industry claims.
Risks stated plainly
Accuracy limits, data concerns and regulatory questions are written down, not glossed over.
A usable next step
The assessment ends with a scoped proposal you can act on, with us or with another team.
Who from our team needs to take part?
Someone who can make decisions about priorities, plus the people who actually run the processes being reviewed. Their time is concentrated in the workshop and a few follow-up conversations.
Do we need to be “AI-ready” before starting?
No. Assessing readiness is part of the work. You need willingness to share how processes run and access to the people involved.
Will you need access to our data?
Usually to samples and system documentation, not production databases. Anything sensitive is handled under an agreed confidentiality arrangement.
What would a first project look like?
Typically a focused pilot on one process with clear examples, measurable quality and limited risk, such as classification, extraction or drafting with review.
Can the assessment conclude that AI is not feasible?
Yes. If a task is better served by plain automation, better data or no change at all, the report says so and explains why.
Next step
Decide with evidence before you fund a build.
Tell us which processes you are considering and what prompted the interest in AI. We will suggest whether an assessment makes sense and what it should cover.
We reply by email. An inquiry is not a contract, and there is no obligation.