AI Consulting & Development
AI that fits the way your business already works
We help you find the AI opportunities worth pursuing, then build secure, practical solutions around them, with people kept firmly in control of the decisions that matter.
AI without the hype
Modern AI is genuinely useful. It's good at reading, summarizing, drafting, classifying and finding information. It's also imperfect: it can be confidently wrong, and it needs good data, careful design and human oversight to deliver value safely.
Our job is to help you use it where it makes sense, avoid it where it doesn't, and explain the trade-offs in plain English along the way. AI is a tool for helping your people do their best work, not a replacement for them.
AI Opportunity Assessment & Strategy
Find the AI opportunities that are worth pursuing, and the ones that aren't, before you invest.
- What it is
- A structured review of your operations, data and goals to identify where AI can make a meaningful difference. We weigh each opportunity's value against its cost, risk and readiness.
- The business problem it solves
- Many organizations know AI is important but aren't sure where to start, which tools to trust or how to avoid expensive experiments that never reach production.
- A realistic example
- A regional distributor wants to "do something with AI." Over a few workshops we map their order handling, customer service and reporting processes, then identify two strong candidates: drafting replies to routine customer emails and summarizing weekly sales performance. Each comes with effort, risk and data requirements spelled out.
- The expected benefit
- A prioritized, realistic roadmap. You invest first in the opportunities most likely to pay off, and you understand the costs, risks and data needed before committing.
How a typical engagement works
- Discovery interviews with the people who know the work best
- Review of current processes, systems and available data
- Opportunity scoring for business value, feasibility and risk
- A written roadmap with a recommended first project and clear success measures
Generative AI Application Development
Purpose-built applications that use generative AI to draft, summarize, classify or transform content inside your workflows.
- What it is
- Custom software that puts generative AI to work on a specific, well-defined task, such as drafting documents, summarizing records or categorizing requests, and that sits inside the tools your team already uses.
- The business problem it solves
- General-purpose chat tools don't know your business, can't follow your templates or rules, and live outside the systems where work actually happens.
- A realistic example
- A professional services firm uses an internal tool that drafts first-pass proposals from a project brief, approved service descriptions and pricing templates. Consultants then review, edit and approve each draft before it goes to a client.
- The expected benefit
- Less time spent on blank-page and repetitive writing, more consistent output, and AI built into everyday tools, with people still responsible for the final result.
How a typical engagement works
- Define the task, the inputs and what a good result looks like
- Prototype using real (anonymized where needed) examples
- Evaluate output quality together with your team
- Build the production application with guardrails, logging and review steps
- Launch to a pilot group, then monitor, measure and refine
Large Language Model (LLM) Integrations
Add intelligent features to the applications you already have, without rebuilding them.
- What it is
- Connecting large language models, from providers such as OpenAI, Anthropic, Microsoft Azure or open-source models, to your existing applications, databases and APIs in a secure and controlled way.
- The business problem it solves
- Your existing systems work, and you'd like to add smarter capabilities without a costly rewrite or locking yourself into a single AI vendor.
- A realistic example
- A support team's existing ASP.NET ticketing system gains "summarize this ticket" and "suggest a category" buttons. Agents see a suggested summary and category, and accept or change them with one click.
- The expected benefit
- Incremental, low-disruption improvements to systems you already depend on, designed so models and providers can be changed as the market evolves.
How a typical engagement works
- Review your architecture, data flows and privacy requirements
- Select a model and provider based on quality, cost and data-handling terms
- Build an integration layer with error handling, usage limits and cost controls
- Test with realistic data, then roll out gradually
Retrieval-Augmented Generation (RAG) & Knowledge Assistants
Assistants that answer questions from your own documents and show where each answer came from.
- What it is
- Retrieval-augmented generation (RAG) finds the most relevant passages in your documents first, then asks the AI to answer using only that material, citing the sources it used. It powers assistants that know your policies, products and procedures.
- The business problem it solves
- Important knowledge is scattered across shared drives, wikis, manuals and inboxes. People lose time searching, or interrupt colleagues for answers that are written down somewhere.
- A realistic example
- An HR team launches an internal assistant that answers employee questions about benefits and leave policies. Each answer links to the exact policy section, and anything the assistant can't answer confidently is passed to the HR team.
- The expected benefit
- Faster, more consistent answers, fewer repeated questions and less time spent searching. Source links mean people can verify what they're told.
How a typical engagement works
- Inventory your content sources and who is allowed to see what
- Build the document ingestion pipeline and search index
- Design answer behaviour, citation style and escalation rules
- Test against real questions collected from your team
- Pilot with a small group, measure answer quality, then expand
Intelligent Document Processing
Turn invoices, forms, contracts and emails into structured, validated data automatically.
- What it is
- A pipeline that reads incoming documents, extracts the information you need, checks it against your business rules and passes it to the right system, combining text recognition, AI and validation logic.
- The business problem it solves
- Staff re-key information from PDFs, scans and email attachments into other systems. It's slow, tedious and prone to errors that cause problems later.
- A realistic example
- An accounts payable team routes vendor invoices through a pipeline that extracts the vendor, dates, totals and line items, matches them against purchase orders and flags discrepancies. Low-confidence results go to a person for a quick review.
- The expected benefit
- Less manual data entry, faster turnaround and fewer keying errors. Confidence thresholds make sure people review the cases that need attention.
How a typical engagement works
- Gather representative sample documents, including messy ones
- Define the fields to capture and the validation rules to apply
- Build extraction and measure its accuracy on your own samples
- Integrate with destination systems and set up a human review queue
AI-Powered Search & Summarization
Search that understands meaning, plus concise summaries of long content.
- What it is
- Semantic search finds relevant information even when people use different words from the original document. Summarization condenses long reports, threads or case histories into the key points, with links back to the source.
- The business problem it solves
- Keyword search misses relevant results when wording varies, and long documents and email threads take too long to read in full.
- A realistic example
- A compliance team searches thousands of past agreements for early-termination terms, even where each contract words them differently, and receives a short summary of each match with a link to the original clause.
- The expected benefit
- People find the right information faster and can understand long material in minutes rather than hours.
How a typical engagement works
- Identify content sources and the questions people need answered
- Build a semantic search index that respects access permissions
- Tune relevance with feedback from real users
- Add summaries that always link back to the original content
Workflow Automation & AI Agents
Software that carries out multi-step tasks across your systems, within clear limits and with approvals.
- What it is
- An AI agent combines a language model with a defined set of tools, such as looking up a record, drafting a message or updating a system, so it can complete multi-step tasks. Each agent has clear boundaries, and people approve important actions.
- The business problem it solves
- Some workflows involve many small judgement calls across several systems. They're too varied for simple rules, but too repetitive to be a good use of skilled people's time.
- A realistic example
- A sales operations agent reviews new website leads, looks up any existing history in the CRM, drafts a personalized follow-up and places it in a queue for a sales representative to approve, edit or discard.
- The expected benefit
- Less coordination and copy-and-paste work, and faster response times. People stay in control of decisions that matter.
How a typical engagement works
- Map the workflow, its decision points and its exceptions
- Define exactly what the agent may do and where approval is required
- Build the agent with limited, audited access to the tools it needs
- Run in "suggest only" mode first and compare results with your team's
- Extend autonomy only where results clearly justify it
Responsible AI, Data Privacy, Security & Human Oversight
The safeguards built into every AI project we deliver.
- What it is
- The practices and controls that make AI safe to use in a business: careful data handling, access controls, quality evaluation, monitoring and clear human accountability for outcomes.
- The business problem it solves
- AI introduces new risks: sensitive data reaching the wrong place, confident answers that are wrong, and uncertainty about who is accountable when something goes wrong.
- A realistic example
- Before launching a customer-facing assistant, we review exactly which data it can access, choose provider settings with appropriate data-retention terms, add content safeguards, test it against difficult and adversarial questions, and set up a process for reviewing flagged conversations.
- The expected benefit
- You can adopt AI with confidence, reduce the chance of costly mistakes, and explain clearly to customers and staff how AI is used and where people stay involved.
How a typical engagement works
- Risk and data review at the start of every AI project
- Access controls, data minimization and guardrails appropriate to the use case
- Evaluation test sets that measure quality before and after changes
- Monitoring, feedback channels and a defined human escalation path
- Plain-English documentation and guidance for your staff. We work alongside your legal and compliance advisors rather than replacing them.
A note on realistic expectations
Every business, dataset and process is different, so we don't promise specific savings or guaranteed results. What we do promise is an honest assessment up front, measurable goals agreed before we build, and transparent reporting on how well a solution performs, including when the right answer is "AI isn't the best fit here."
Often the most valuable improvement is a simpler one. See how we approach business process automation or custom web applications.
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