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Choose a practical starting point for AI.
Review your workflows, data and constraints before committing to a larger implementation. Compare opportunities using expected usefulness, data readiness and the effort needed to operate the result.
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Overview
AI Consulting, explained
AI consulting should end with decisions you can act on, not a slide deck of possibilities. We help leadership and product teams identify where AI can make a measurable difference, assess data and technical readiness, and choose a first pilot that is small enough to deliver quickly and meaningful enough to matter.
Because we also build software, our recommendations are grounded in what it takes to implement and operate a solution: integration effort, data preparation, running costs, risks and the skills your team needs. You can take the plan to any team, or continue with us into the pilot.
Before you build
Decisions worth settling first
Settling these points early keeps scope realistic and makes the estimate meaningful.
01
Business goals
Clarify which outcomes matter most, such as cost, speed, quality or customer experience.
02
Candidate processes
List workflows with repetitive work, large volumes or decisions based on scattered information.
03
Data landscape
Review what data exists, its quality, ownership and any restrictions on use.
04
Risk appetite
Agree where automation is acceptable and where people must stay in control.
05
Budget and timeline
Set realistic expectations for pilot and production investment.
06
Team and ownership
Identify who will own AI features internally after launch.
Services
What our AI consulting services include
AI opportunity assessment
Structured review of processes to find and rank AI use cases by value and feasibility.
Data readiness review
Assessment of data sources, quality, access and gaps for priority use cases.
Architecture and vendor advice
Recommendations on models, platforms, hosting and build-versus-buy choices.
Pilot definition
Scope, success measures, evaluation plan and cost estimate for a first pilot.
AI governance basics
Practical policies for data use, review, monitoring and responsible deployment.
Team enablement
Workshops that help product and engineering teams work effectively with AI tools.
How it is built
The layers behind a dependable result
Every layer has an owner, tests and documentation, so the system can be supported after launch.
01
Discover
Interviews and workshops to understand goals, processes and constraints.
02
Assess
Technical and data review of shortlisted opportunities.
03
Prioritise
Scoring by value, feasibility, risk and cost to select the first initiatives.
04
Plan
A roadmap with pilot scope, architecture outline, estimates and next steps.
Use cases
Where it helps
Leadership teams
Decide where to invest in AI and in what order.
Product teams
Identify AI features that strengthen an existing product.
Operations
Find automation opportunities in back-office workflows.
Agencies
Validate AI ideas for client projects before proposing them.
Startups
Shape an AI-first product idea into a buildable MVP scope.
IT departments
Plan secure adoption of AI tools across the organisation.
Illustrative use case
A discovery review can compare document processing, internal search and customer support use cases before selecting one pilot.
Why PoliteTech
Why teams choose PoliteTech
Scope before commitment
Priorities, assumptions and acceptance criteria are agreed in writing before a delivery plan is fixed.
Engineers who build
Recommendations come from the people who design, code and support the software, so they reflect real implementation effort.
Visible progress
A shared backlog, regular demos and recorded decisions keep you informed without chasing updates.
Practical AI
Evaluation sets, review steps, cost limits and monitoring are part of every AI feature we ship.
Flexible engagement
Start with a scoped project, a dedicated team or support, and change the model as your roadmap evolves.
Clear ownership
Repositories and accounts can live in your organisation, with IP and confidentiality terms agreed in the contract.
Industries
Where this work fits
Retail and Ecommerce
Build web and mobile journeys around product discovery, ordering and customer service.
Financial Software
Support reporting, document processing and internal operations through defined application workflows.
Logistics Software
Connect planning, task assignment and operational updates in one workflow.
Education and Elearning
Create platforms for content, activities and progress tracking.
Fitness and Wellness Apps
Support tracking, coaching content and user engagement around a clear product goal.
Manufacturing Software
Replace fragmented tracking with workflows for quotations, work status and supporting documents.
Real Estate Applications
Create listing, enquiry and internal coordination tools around your business process.
Business Operations
Build task, approval and reporting tools around the way your team works.
Process
A practical path from requirements to release.
Each stage ends with something you can review: a scope, a prototype, a working increment or a release.
01
Understand
Discuss your users, business needs, existing systems and delivery constraints.
02
Define
Agree scope, technical approach, responsibilities and a delivery plan.
03
Design and build
Develop in manageable increments with visible progress and review points.
04
Test and release
Validate agreed workflows, resolve release blockers and prepare deployment.
05
Support and improve
Document the handover, review production feedback and plan the next priorities.
Technology
Tools and platforms we work with
OpenAIGoogle GeminiLlamaMistralAzure AIAWS BedrockGoogle Vertex AIPythonLangChainLlamaIndexPower AutomateZapiern8n
Cost
What shapes the cost of your project?
There is no useful price without scope. These are the factors we review when preparing an estimate, and the estimate lists every assumption so you can see what would change it.
01
Number of workflows
Each workflow needs its own prompts, tools, evaluation set and review design.
02
Data preparation
Cleaning, labelling, chunking and access rules often take more effort than the model itself.
03
Integrations
Connections to CRMs, ERPs, document stores and identity providers add design and testing work.
04
Evaluation and review
Building test sets, review queues and monitoring is essential for reliable results.
05
Model and hosting
Hosted APIs, open models and private deployment have very different setup and running costs.
06
Security and compliance
Sensitive data, audit needs and data residency add controls and documentation.
Engagement models
Choose the engagement that fits your next step.
Dedicated Team
For a changing roadmap or ongoing development. Agree roles and working process, then manage priorities through a shared backlog.
Best for team extension and long-term product work.
Scoped Project
For a defined product or milestone. Align on requirements, deliverables and acceptance criteria, with changes reviewed before they affect the plan.
Best for MVPs, integrations and focused releases.
Maintenance and Improvement
For software that already exists. Start with a technical review, then agree support routines and planned improvements.
Best for live applications and modernisation.
FAQ
Questions about AI consulting
What do we receive at the end?
A prioritised list of use cases, a recommended first pilot with scope and success measures, an architecture outline and an estimate.
Do we have to build with you afterwards?
No. The recommendations are yours to use with any team, although we are happy to continue into delivery.
Do you recommend specific vendors?
We stay vendor-neutral and compare options on your requirements, including open-source and hosted services.
How long does an assessment take?
It depends on the number of processes and stakeholders. A focused assessment can be short; a wider review takes longer. We agree the scope first.
What if our data is not ready?
We identify the gaps and suggest practical steps, which sometimes means starting with a use case that relies on documents rather than structured data.
Can you help with AI policy?
Yes, at a practical level: data handling, review steps and monitoring. For legal advice we recommend your own counsel.
Can we begin with a focused scope?
Yes. We can review your current situation and agree a bounded first engagement before a wider build.
How are pricing and timelines determined?
We review requirements, technical complexity, team needs and delivery constraints before proposing an engagement. Estimates list assumptions and what may change the plan.
Can we discuss a project under an NDA?
Yes. Let us know before sharing confidential materials so we can agree confidentiality terms first.
Discuss your AI consulting needs
Share your goals, current situation and the help you need. We will review your message and suggest a practical next step.