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ServicesAI Adoption

One working thing, not an AI strategy.

Most consultancies advise. We build. Smart Solid Solutions is a technology consultancy that qualifies AI work harder than it sells it. We find the one workflow where a model earns its place, build it into the systems your people already use, and measure it against criteria agreed before we start. If there is no such workflow, we say so.

Book a Technical Review Call See how we do it

What we typically see

None of those is a plan. Together they create pressure — and pressure can lead to bad purchases.

It usually arrives as several things at once.

  • The board’s question The board has asked what the business is doing about AI.
  • A competitor’s announcement A competitor has announced something — a chatbot, a “copilot”, an “AI-powered” line in the brochure.
  • A vendor’s pitch A software vendor is pitching an AI module on top of the system you already pay for.
  • Customer data in a free tool And somewhere in the building, someone is pasting customer data into a free tool to get reporting done faster.

The combined pressureDifferent sources. Same result.

Your actual workflows Worth doing?
What does it cost to find out?
How exposed are you?

The possible outcomesWhen decisions are made under pressure.

  • Build One workflow where a model earns its place.
  • Fix the process first A model would do the wrong thing faster.
  • Not yet If there is no such workflow, we say so.

What you actually need

A straight answer.

We examine the real workflows and tell you — including when the answer is “not yet”.

  • Worth doing? Is there anything here worth improving?
  • Cost to prove? What will it cost to find out?
  • Any exposure? Are you exposed by the tools already in use?
Talk to an expert

Practical. Technical. No obligation.

Practical AI adoption

AI adoption needs a framework. Ours starts with the work.

Smart Solid Solutions gives AI adoption a practical structure: start with real workflows, define what success means, use the right technology, and require evidence before scaling.

01The difference

Two ways to approach AI.
Very different outcomes.

Start with the workflow and get real outcomes.
Start with AI and you often end up with an impressive demo — but no impact.

Smart Solid Solutions approach

From real workflows to real outcomes.

  1. Find a workflow Start with a concrete, valuable use case.
  2. Set a success criterion Define what “good” looks like.
  3. Get evidence Test, measure and learn.
  4. Build / Fix / Not yet Put it in use, improve it — or don’t.

Practical progress. Without the guesswork.

AI-first approach

Start with AI. Looks impressive. Delivers little.

  1. Demo A few hand-picked examples.
  2. Pilot No clear success criterion, no owner.
  3. Purgatory Never killed and never shipped.
  4. A year later The board asks again.

Good intent. No impact.

02Why we start there

Not every problem needs AI.

Much of what is sold as AI adoption is not needed. And where AI is already in use, the common failure is delivery that has outrun governance: the weakest foundation, not the strongest, sets what you can safely run.

  • No data

    AI can’t invent the foundation. If the data isn’t there, a model won’t create it.

  • Bad process

    AI won’t fix the workflow. It will do the wrong thing faster.

  • A rule will do

    Don’t use AI. Deterministic code is cheaper, auditable and predictable.

03How we decide

Find the workflow.
Define success.
Get evidence.

We work with your team to identify the right use cases, define what success looks like, test them against real work, and decide whether to build, fix or stop.

  1. Find the workflow Is there a real, valuable use case?
  2. Define success What result would justify the investment?
  3. Get evidence Test it against real work.
  4. Decide Build · Fix · Not yet.

04How we build

Models for judgement. Code for guarantees.

We use the right tool for the right problem — language models where they add real judgement, and deterministic code where correctness and control are non-negotiable.

Use a model for

Judgement over messy inputs.

  • Unstructured documents
  • Free text
  • Classification that needs nuance
  • Complex, context-dependent reasoning

Use code for

Deterministic guarantees.

  • Calculations
  • Business rules
  • Anything that must always be right
  • Auditable and predictable outcomes
Next: our Five Gates framework. A disciplined way to qualify, design, build and prove every AI investment.
See the Five Gates

How we do it

Our Five Gates framework.
Built to stop money being spent on the wrong thing.

Every AI engagement at Smart Solid Solutions passes through five gates. Each answers a specific question — and must justify the investment in the next one.

Qualify

Is this worth investigating?

We run a structured assessment of how your organisation actually works with AI, scored by our own code. It gives a level for four foundations and one clear answer: proceed to Discovery, or stop here.

  • VelocityHow fast the organisation learns, decides and delivers with AI.
  • IntelligenceThe people, the systems and the organisational capability behind it.
  • GovernanceWhether AI is used responsibly, safely and in a way that lasts.
  • ScaleWhether the gains compound across the organisation, or stop at one team.
  • Scored by code, not by a model.The same answers give the same result every time.
  • Some absences close the gate.If key foundations are missing, we stop.
  • Not a strategy deck.A clear, evidence-based decision.

Discover

What would success
actually look like?

We map the workflow as it is really done, not as the process document says. We define success criteria in numbers your team would accept — time, error rate, cost per item — and a kill criterion: the result at which we recommend you stop. All criteria are agreed in writing before any code.

From current state to a defined target

  1. Current workflow
  2. Success criteria
    • Time
    • Error rate
    • Cost per item
  3. Clear decision
    • Build
    • Fix
    • or Stop
  • Real workflowUnderstand how the work really happens, including manual steps and exceptions.
  • Success criteriaAgree measurable outcomes in numbers your team would accept.
  • Kill criterionDefine the result at which we recommend you stop.
  • Criteria agreed in writing before any code.No code is written until success and stop criteria are signed off.

Design

What can the model do —
and what must it never do?

We work out what the model can do, where it fits in the workflow, and what must never happen. We define the architecture, set the guardrails and make sure the right people stay in control.

The key focus areas

  • ArchitectureHow the model works and integrates.
  • Human approvalWhere people stay in the loop.
  • Data boundariesWhat data can and can’t be used.
  • UK GDPRLegal and regulatory requirements.
  • OutputA buildable, governed specification.
  • DecisionGo to Build, or stop if there are unacceptable risks.
  • Built for the real worldNot just what the model can do, but what’s safe, legal and useful in practice.

Build

Put it inside the real workflow.

We build the workflow into the systems your people already use — the ERP, the ticketing queue, the shared mailbox. Engineering, not prompting: we run a full evaluation set from your real cases, put guardrails on inputs and outputs, and set a cost ceiling so the system cannot exceed it. If the model is unavailable or unsure, a deterministic fallback takes over.

From design to real-world software

  1. IntegrateInto your existing systems
  2. EvaluateRun on real cases
  3. ProtectGuardrails and cost controls
  4. DeployReliable and usable
  • Existing systemsBuilt into the ERP, ticketing queue, shared mailbox, etc.
  • Evaluation setRun on your real cases, not examples.
  • GuardrailsControls on inputs and outputs.
  • Cost ceilingThe system cannot exceed its cost.
  • FallbackDeterministic fallback when the model is unavailable or unsure.
  • Engineering, not prompting.Real software, tested against real cases, with controls that make it safe and sustainable.

Prove

Did it actually work?

We run the solution beside you, measure it against the agreed criteria, and decide together whether to scale, improve or stop. We hand over the code, the evaluation set and the runbook, so you can operate it with confidence.

From pilot to decision

  1. MeasureCompare to the agreed criteria
  2. ReviewLook at results together
  3. HandoverCode, evaluation set and runbook
  4. DecideScale, improve or stop
  • MeasureRun it beside you and compare results to the Discovery criteria (time, error rate, cost per item).
  • HandoverTransfer the code, evaluation set and runbook.
  • Decide togetherScale, improve or stop based on evidence, not optimism.
  • Operate with confidenceYou have everything needed to run, monitor and iterate.
  • A second workflow is only worth doing if the first one works.We don’t move to the next opportunity until the results justify it.

Scope and stopping points

The engagement track

Each step is scoped on its own and is a valid stopping point; the assessment often ends with the recommendation not to continue.

Step 0 · Entry point

Technical Review Call

The board's question, the vendor's pitch, what is already in use.

You getWhether AI Adoption is the right track at all

Book a Technical Review Call 45 minutes, no charge

Every engagement is scoped and quoted before it starts; there is no rate card because there is no standard job.

From pilot to production.

AI Adoption

  1. AI Readiness & Technical-Fit Assessment Phase 0 and Discovery: candidate workflows, data audit, risk class, success and kill criteria You getA written verdict: build, fix the process first, or don't
  2. One Real Workflow pilot Design and Implementation of one workflow against the pre-agreed criteria You getA working system on your real data, an evaluation set, measured results
  3. Production Rollout & Handover Hardening, integration, training and Hypercare You getThe workflow live, documented and owned by your team
  4. AI Governance Optional Evaluation drift, cost, model changes, vendor claims, policy You getA named technical owner for AI on your side of the table

Proof

We build it. We govern it.
And we show the evidence.

No invented case studies. We show the work we can substantiate — what was built, what was governed, and what the evidence showed.

See all engagements

We build it

Founder-built

Our own AI-enabled delivery tooling

We use language models, deterministic code and validation to deliver real tooling for our own work.

  1. Code Static analysis produces the evidence.
  2. Evidence Structured data from real repositories.
  3. Model judgement Language models reason over the evidence, not the other way round.
  4. Validation Independent validators check outputs.

First live run

2 of 4 reviewer agents caught fabricating commit hashes.

We govern it

In progress

Independent oversight of an outsourced AI build

Our founder has governed an outsourced AI product build for a client, ensuring compliance, control and evidence at every stage.

  1. Supplier build AI product developed by external team.
  2. Independent oversight Technical, legal and operational review.
  3. Board evidence Objective reporting on progress, risks and outcomes.
  • GDPR and EU AI ActCompliance for biometric processing.
  • IP and dataData-ownership terms.
  • Supplier progressVerification and milestones.

Result

Evidence-based board oversight.

A good fit looks like this

One workflow. Real data. Agreed criteria.

We find the one workflow where a model earns its place, build it into the systems your people already use, and measure it against criteria agreed before we start.

  • AI has become a real business obligation, not an experiment The board asking, a vendor pitching, a competitor announcing — or a free tool already in use.
  • Built into the systems your people already use The ERP, the ticketing queue, the shared mailbox — not a chatbot on the side that nobody opens.
  • Measured against criteria agreed before we start Success criteria in numbers your finance team would accept, and a kill criterion.
  • Something funded, something forcing the question An outside obligation is forcing the question; this is the right service when one is.

Who this is not for

If there is no such workflow, we say so.

We qualify AI work harder than we sell it.

  • A business that wants a strategy document and no build.
  • A team that wants a chatbot because a competitor has one.
  • Anyone who needs the answer to be "yes" before we have looked.
  • Pre-revenue startups.
  • Nothing funded, nothing forcing the question. If no outside obligation is forcing the question, this is not the right service yet.

Get started

Is the board asking about AI?

If one of the triggers above is live — a deal, a departure, a stalled migration, a board asking about AI — the next step is a 45-minute Technical Review Call, at no charge. We will tell you whether there is work here and, if so, what the first fixed-scope step is. If there is not, we will tell you that too.

  • Technical discussion Focused on your system and your goals.
  • With senior experts Practical input from experienced engineers.
  • Clear next steps You’ll know where you stand after the call.
Book a Technical Review Call

Practical. Technical. No obligation.