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Customer and employee assistants
The right answer and a clear next step.
Repeated questions overload support teams, while employees struggle to locate current instructions. Select tasks, audiences and permitted sources before building a prototype. Evaluate it against realistic questions and define when uncertainty, a sensitive request or a missing answer requires a colleague to take over.
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When it helps
Repeated questions overload support teams, while employees struggle to locate current instructions.
What we do
- Website and internal assistants
- Question routing and contact information collection
- Human handover with conversation context
How it works
Select tasks, audiences and permitted sources before building a prototype. Evaluate it against realistic questions and define when uncertainty, a sensitive request or a missing answer requires a colleague to take over.
What you receive
- Assistant with an agreed scope of answers
- Evaluation set, operating instructions and team handover
Illustrative example
Illustration: a visitor describes a problem, the assistant finds instructions and prepares a summary for support.
How we measure success
- Answer accuracy on the agreed test set
- Successful handovers and time to a useful response
After launch
After launch, monitoring of quality, costs and model changes is agreed separately. We establish ownership, instructions and a procedure to restrict or stop the solution.
Company knowledge search with RAG
Answers grounded in your sources.
Instructions, catalogues and accumulated knowledge are scattered across documents, making it difficult to identify a current, reliable answer. Establish source ownership, connect retrieval and test answer quality alongside user permissions. RAG supplies relevant information to a model at response time; connecting company documents does not by itself train or fine-tune the model.
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When it helps
Instructions, catalogues and accumulated knowledge are scattered across documents, making it difficult to identify a current, reliable answer.
What we do
- Document inventory and access permissions
- Semantic search with source references
- Index updates and missing-answer handling
How it works
Establish source ownership, connect retrieval and test answer quality alongside user permissions. RAG supplies relevant information to a model at response time; connecting company documents does not by itself train or fine-tune the model.
What you receive
- Knowledge search with links to source passages
- Refresh rules and evaluation report
Illustrative example
Illustration: a technician finds the procedure for a product version and opens the relevant section of its current manual.
How we measure success
- Relevance of retrieved sources
- Citation accuracy and permission compliance
After launch
After launch, monitoring of quality, costs and model changes is agreed separately. We establish ownership, instructions and a procedure to restrict or stop the solution.
Documents, email and drafts
From incoming files to checked, usable data.
Reading attachments, entering fields and preparing replies manually slows processing and increases the chance of transcription errors. Use representative documents to define fields, validation rules and exceptions. Connect incoming channels, flag uncertain results and agree on review before records enter a business system or a response is sent to a customer.
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When it helps
Reading attachments, entering fields and preparing replies manually slows processing and increases the chance of transcription errors.
What we do
- OCR and data extraction from PDFs and images
- Classification, summaries and required-field checks
- Response drafts with human approval
How it works
Use representative documents to define fields, validation rules and exceptions. Connect incoming channels, flag uncertain results and agree on review before records enter a business system or a response is sent to a customer.
What you receive
- Document workflow with agreed controls
- Example set and exception-handling rules
Illustrative example
Illustration: an enquiry attachment becomes a structured CRM draft that a salesperson checks before approval.
How we measure success
- Accuracy of individual extracted fields
- Processing time and proportion requiring corrections
After launch
After launch, monitoring of quality, costs and model changes is agreed separately. We establish ownership, instructions and a procedure to restrict or stop the solution.
AI agents and tool connections
From conversation to a controlled action.
An assistant can suggest a solution, but completing it still involves switching between applications and entering the same information again. Break the process into permitted steps, identify actions requiring approval and start in a test environment. Evaluate incomplete instructions, connection failures and disallowed requests as well as successful scenarios before enabling agreed tasks.
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When it helps
An assistant can suggest a solution, but completing it still involves switching between applications and entering the same information again.
What we do
- Connections to APIs, CRM, calendars and documents
- Restricted permissions, approvals and execution logs
- Error handling, cost limits and a stop procedure
How it works
Break the process into permitted steps, identify actions requiring approval and start in a test environment. Evaluate incomplete instructions, connection failures and disallowed requests as well as successful scenarios before enabling agreed tasks.
What you receive
- Connected agent with bounded permissions
- Logs, evaluation cases and a shutdown procedure
Illustrative example
Illustration: an agent gathers customer information and creates a CRM task after an employee approves it.
How we measure success
- Successfully completed permitted tasks
- Errors, required approvals and cost per task
After launch
After launch, monitoring of quality, costs and model changes is agreed separately. We establish ownership, instructions and a procedure to restrict or stop the solution.
Images, speech and AI model training
Choose the model to fit the task and data.
A general model may not achieve the required quality for specialist language, image recognition or speech in your working environment. Compare a base model, improved instructions and RAG first. Train further when data and measurements justify the cost; organise labelling and separate training from test examples. Training a large foundation model from scratch requires a separately scoped research project.
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When it helps
A general model may not achieve the required quality for specialist language, image recognition or speech in your working environment.
What we do
- Computer vision, speech transcription and classification
- Fine-tuning existing models where supported
- Specialist neural networks with independent evaluation
How it works
Compare a base model, improved instructions and RAG first. Train further when data and measurements justify the cost; organise labelling and separate training from test examples. Training a large foundation model from scratch requires a separately scoped research project.
What you receive
- Pilot or adapted model with an evaluation report
- Dataset versions, limitations and deployment recommendation
Illustrative example
Illustration: compare product-photo classifiers and evaluate their performance on new photographs outside the training set.
How we measure success
- Accuracy and errors by class
- Response time and inference cost
After launch
After launch, monitoring of quality, costs and model changes is agreed separately. We establish ownership, instructions and a procedure to restrict or stop the solution.
AI strategy, adoption and reliable operation
From a useful first case to everyday use.
Isolated AI experiments deliver limited value without a connection to business processes, accountable owners and the team's working practices. Select a use case with management and users, then assess data, risks and total costs. Compare the pilot with a baseline, establish ownership and human oversight, and agree on evidence required to expand, change or stop the solution.
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When it helps
Isolated AI experiments deliver limited value without a connection to business processes, accountable owners and the team's working practices.
What we do
- Opportunity, readiness and priority assessment
- Cloud or local deployment with data-use rules
- Training, evaluation, monitoring and version management
How it works
Select a use case with management and users, then assess data, risks and total costs. Compare the pilot with a baseline, establish ownership and human oversight, and agree on evidence required to expand, change or stop the solution.
What you receive
- Adoption roadmap and success criteria
- Pilot, user training and monitoring agreement
Illustrative example
Illustration: a support team pilots response drafting and expands use only after the agreed quality and efficiency checks.
How we measure success
- Time saved against the baseline
- Quality, actual adoption and cost per task
After launch
After launch, monitoring of quality, costs and model changes is agreed separately. We establish ownership, instructions and a procedure to restrict or stop the solution.
Scope and delivery options
Custom models, local deployment and model adaptation follow an initial assessment of data, hardware and cost. Important decisions receive agreed human oversight.
Technologies and approaches
- LLM
- Language models for understanding and generating text.
- RAG
- Answers supported by retrieval from your sources.
- API / MCP
- Ways to connect an assistant to permitted tools.
- OCR / multimodal AI
- Document reading and work with text, images or speech.
- Machine and deep learning
- Learning from examples; model selection and adaptation are assessed in a pilot first.
- MLOps / LLMOps
- Version management, change evaluation and monitoring of AI quality and cost.
