Knowledge-base assistants
Help users find answers in approved company documents, product information or operating guidance, with source references where available.
AI AGENT DEVELOPMENT & INTEGRATION
AI agents.
Connected to your business.
We develop business AI assistants and agents around approved information and clearly defined tasks. Projects can combine a company knowledge base, model APIs, workflow tools and integration with a website, app or existing system.
Which AI agent use cases can we explore?
Website service assistants
Answer common questions, explain services and route a visitor to the appropriate human contact when the answer is uncertain.
Internal workflow agents
Connect defined tasks to approved tools and APIs, with permissions and human confirmation for actions that require review.
Model and data work
Assess model integration, data collection and preparation, evaluation, model development or fine-tuning when the problem requires it.
What is the difference between an assistant and a workflow agent?
An assistant primarily answers or drafts information; a workflow agent may also use tools to carry out defined steps in a business process.
The distinction affects permissions, logging and acceptance criteria. An assistant that answers questions from a document library needs a reliable content source and an escalation path. An agent that creates a record or updates a system also needs controlled access, validation and a clear approval boundary.
We start with a specific user task instead of an open-ended promise of automation. The proposal identifies what the system may read, what it may change and which decisions remain with your team. A website or app can provide the interface while your existing systems remain the source of record.
Do we need fine-tuning or a knowledge base?
The right approach depends on the task and the information. We assess retrieval from a knowledge base, prompting, tool integration and fine-tuning against the actual requirements.
Frequently changing business documents often call for an updateable retrieval workflow. A specialised task may require evaluation of model behaviour, examples and training data. Fine-tuning is a project decision to justify through evaluation, not a default requirement for every assistant.
How is an AI agent project delivered?
- 01
Define the task
Identify users, useful outcomes, source data and the actions the agent may take.
- 02
Prepare & connect
Review content, access rules, model options and the required interfaces.
- 03
Evaluate
Use representative questions and tasks to inspect answers, references, tool use and failure cases.
- 04
Deploy & improve
Agree hosting, operating costs, monitoring, updates and human escalation.
What should be evaluated before release?
Use representative business tasks and explicit acceptance criteria to assess whether the agent is useful and where human review is required.
- Whether answers use the approved sources and reflect their current content.
- How the agent handles missing, conflicting or out-of-scope information.
- Whether access restrictions and tool permissions behave as intended.
- How failed requests, model limits and integration errors are reported.
- What model usage, hosting and support costs the agreed workload creates.
Questions before we start
Can an agent use our internal documents?
Yes, subject to document access, data quality and the chosen deployment design. We first identify which information is approved for the intended users and how updates should be managed.
Can it work with our website, app or CRM?
We assess the available APIs and permissions. A tool integration must define the allowed operations, validation rules, logging and any required human approval.
Do you support a particular model provider?
We review the required models, deployment region, API availability and data-handling requirements before selecting an integration. Provider accounts, usage charges and access conditions should be stated separately.
Can we use private or self-hosted deployment?
Deployment options depend on model licensing, infrastructure, data requirements and operating resources. We can review these constraints before proposing a suitable architecture.
Can you guarantee perfect answers?
No. The design should include evaluation, source grounding where suitable, uncertainty handling and a route to human support. Acceptance criteria must reflect the real tasks and known limitations.
What should we send for an initial discussion?
Describe one workflow, its users and desired outcome. Include sample questions, the types of documents involved, systems to connect and any restrictions on data or deployment.
START A CONVERSATION
What would you like to build?
Tell us about your customers, the problem to solve and the systems you already use.