We build AI into your products, services and business operations. Assistants can research information, prepare responses, analyse data and use your software tools to carry work through several steps. We develop and test the solution around your goals, using a pilot when useful, and measure accuracy, time saved and running costs. Your team controls the level of automation through defined permissions and review points.
Use an allowed tool to look up information and draft a response.
Connected toolApproved business system
03 / Human decision
Your team decides
Check the proposal, then approve, edit or decline it.
One agent workflow, with permissions and review designed around the task. AI also supports search, analysis, processing and model adaptation.
//WHO IS THIS FOR
//WHAT IS INCLUDED
Scope of work
01
AI agents and business workflows
Connect AI to your tools so it can complete useful work. Example: triage an incoming enquiry, look up customer details and prepare a response for staff approval.
[01]Staff and customer-facing assistants connected to your applications and APIs
[02]Agentic workflows where AI plans steps and uses tools with defined permissions
[03]Routing requests, preparing follow-ups and updating records
[04]Approval steps, action logs and recovery when a task fails
02
Knowledge search and grounded answers
Help staff and customers find useful information across documents, products and business records. Example: answer a policy question with links to the relevant source passages.
[01]Retrieval-augmented generation (RAG): finding relevant sources to support an AI answer
[02]Semantic search using embeddings to match meaning alongside keyword search
[03]Search across knowledge bases, documents and approved business data
[04]Source references, access controls and handling missing information
03
Document and data processing
Turn incoming material into information your systems can use. Example: extract invoice details, flag missing fields and prepare a record for review.
[01]Extraction, classification and summarisation of documents and PDFs
[02]Audio and video transcription and analysis
[03]Structured outputs with field validation and exception handling
[04]Data preparation and connections to existing systems
04
Evaluation and measurable improvement
Check whether the system completes the task well and improves the process. We agree success criteria and compare results with your current workflow.
[01]Evaluations (evals): repeatable tests using representative tasks and difficult cases
[02]Measuring accuracy, task completion, time saved and staff corrections
[03]Tracking cost per task, response time and workflow failures
[04]Testing changes to prompts, models and tools before release
05
Model adaptation and fine-tuning
Improve consistency for a specialised task or output format. We test prompts and examples first, then assess whether fine-tuning offers a worthwhile improvement.
[01]Model selection and prompt design for your use case
[02]Fine-tuning and model adaptation using curated training examples
[03]Preparing training data and separate evaluation datasets
[04]Comparing quality and operating costs before adopting a model
06
Deployment and ongoing operation
Fit the solution to your data requirements, infrastructure and budget, with a clear plan for running and maintaining it.
[01]Cloud, local and on-premises deployment options
[02]Data handling, provider retention settings and access permissions
[03]Monitoring, usage limits and agreed human review
[04]Documentation, handover and ongoing improvement
Illustrative workflow
How document-based answers work
A question leads to a search for relevant passages. Those passages help a model draft an answer with source references. Test cases and human review help check the result; AI can still make mistakes.
Illustrative workflow
01ASKyour question
02FINDrelevant passages
03CONTEXTsource material
04ANSWERwith references
//FAQ
Frequently asked
Possible uses include researching information, handling customer enquiries, updating business records, processing documents and preparing reports from several systems. These are illustrative examples. We design the workflow around the outcome you want, connect the relevant tools and test it with your team.
They are related. Embeddings represent information numerically so search can match meaning rather than just exact words. RAG uses retrieved information to help an AI model produce an answer. Embeddings often support the search step within RAG, and are also useful for standalone search and similarity matching. We treat these as parts of a solution chosen for your task.
We agree a baseline and success criteria, then test representative tasks and difficult cases. Depending on the workflow, measures include accuracy, completion rate, staff corrections, processing time and cost per task. We use those results to guide improvements and decide when the solution is ready for wider use.
Your team decides which actions can run automatically and which need approval. We configure tool access, data permissions and review points around those decisions. Logs, validation and monitoring help detect and recover from errors. AI can still make mistakes, so testing and review requirements are part of the design.
That depends on the task and your data requirements. Fine-tuning adapts model behaviour using examples; retrieval supplies relevant information when the system needs it. We compare simpler approaches first and discuss cloud or local deployment, provider data handling, hardware needs and running costs before recommending a setup.
Tell us about your project, your systems or the problem you want to solve. Share any goals, timing or budget you have in mind, and we’ll discuss the next steps.