Build · Service
AI agents and assistants
that understand the work around the conversation.
We design and engineer AI assistants and agentic systems that can retrieve context, use tools, interact with business systems and support multi-step work — with explicit boundaries around what they can know, decide and do. Assistants help users; agents can also act across tools and systems.
When an AI assistant becomes more than a chatbot
Useful assistants do not just answer questions. They understand enough context to help work move forward.
A conversational interface can be useful on its own, but the larger opportunity appears when the system can understand the user’s task, retrieve the right information and interact with the applications or workflows behind the conversation.
- 01People spend too much time finding the right informationKnowledge exists across documents, systems and teams, but locating and interpreting it interrupts the actual work.
- 02Users need guidance across complex processesThe task involves several steps, policies, choices or systems, and people repeatedly need help understanding what comes next.
- 03Work moves between conversation and business systemsA user can explain what they want in natural language, but the actual outcome still requires searching, creating, updating or triggering something elsewhere.
- 04Existing chatbots answer questions but cannot complete the jobThe interface may be conversational, but the system lacks enough context, tool access or workflow awareness to become genuinely useful.
The agent decisions
Before giving an AI more autonomy, decide what role it should actually play.
- 01Who is the agent helping?Which customer, employee, operator or specialist is using it, and what part of their work should it support?
- 02What should it know?Which documents, records, application state, business rules and historical context are necessary for useful responses?
- 03Which tools should it be allowed to use?What can the agent search, calculate, create, update, submit or trigger through APIs and connected systems?
- 04What should require explicit approval?Which actions affect money, customers, sensitive data, business records or other consequential outcomes?
- 05How should multi-step work be controlled?When should the agent plan several actions, ask for clarification, pause, retry or hand control back to a person?
- 06How will useful behaviour be evaluated?What does success look like across answer quality, task completion, tool use, safety, latency and cost?
What AI Agents & Assistants can include
The right agent can answer, guide, retrieve and act — without pretending every task deserves autonomy.
- 01Employee assistantsInternal copilots that help teams find information, understand processes, draft work and navigate business systems.
- 02Customer assistantsContext-aware support and service experiences that help customers resolve questions or complete tasks.
- 03Knowledge assistantsAssistants grounded in controlled documents, policies, records and organisational knowledge.
- 04Operational agentsAgentic workflows that analyse context, use tools and move selected operational tasks through multiple steps.
- 05Product copilotsAI assistance embedded inside SaaS, web or mobile products to help users complete complex product tasks faster.
- 06Specialist assistantsDomain-specific systems designed around focused roles such as sales, service, operations, research or internal support.
Related AI & platform areas
AI agents depend heavily on the systems and information around them. These areas commonly determine whether the assistant is genuinely useful or simply conversational.
Engagement scope
What we actually do.
- 01Agent role & workflow definitionDefine the users, tasks, responsibilities, success criteria and boundaries around what the assistant should and should not do.
- 02Conversation & interaction designShape how the system asks questions, explains decisions, surfaces uncertainty and hands control back to the user.
- 03Context & retrieval engineeringConnect the assistant to relevant documents, records, databases, application state and business knowledge.
- 04Tool & action integrationGive the agent controlled access to APIs, search, calculations and business-system actions required to complete useful work.
- 05Guardrails, evaluation & approvalDefine action boundaries, human approvals, test scenarios and evaluation criteria around quality and behaviour.
- 06Production operationImplement logging, observability, cost tracking, access controls and ongoing evaluation once the assistant is used by real people.
Engagement options
Ways we can build with you.
Not sure which starting point fits? We will help you choose.
- Purpose-built assistantDesign and build one assistant around a clearly defined user group and task.Useful whenThe organisation has one high-value knowledge or workflow problem that deserves a focused implementation.
- Agentic workflowBuild an agent capable of using several tools or systems across a controlled multi-step process.Useful whenWork currently requires people to interpret information and coordinate actions across several systems.
- Embedded agent engineeringAdd AI-agent capability alongside an existing product or technology team.Useful whenThe organisation already has an AI product direction but needs deeper engineering around retrieval, tools, evaluation or orchestration.
What you leave with
An assistant that understands enough of the work to be genuinely useful.
The exact outputs depend on the role and workflow, but the engagement should leave the organisation with a production capability whose context, actions and boundaries are explicit.
- 01A defined agent roleClear responsibilities, users, goals and boundaries around what the assistant is expected to handle.
- 02Connected contextAccess to the documents, records and application state required to respond with relevant information.
- 03Controlled tool accessDefined APIs and business actions the system can use without giving it unnecessary authority.
- 04Explicit approval pointsHuman review or confirmation where actions become consequential or uncertainty matters.
- 05Evaluation evidenceRepresentative scenarios and measures for answer quality, task completion and tool behaviour.
- 06Observable production behaviourLogging, usage, cost, failure and escalation signals visible enough to improve the assistant over time.
Our engineering approach
Give the agent enough capability to help — and enough boundaries to remain predictable.
- 01Define the roleClarify who the assistant helps, what task it owns and where responsibility stops.
- 02Build the contextConnect the knowledge, data and system state required for useful reasoning.
- 03Add tools deliberatelyGive the assistant only the actions required to complete the defined workflow.
- 04Test behaviour and boundariesEvaluate realistic conversations, tool use, failure modes and escalation paths.
- 05Release and improveObserve real interactions and refine prompts, retrieval, tools, controls and workflow behaviour using evidence.
From assistant to operating capability
A useful conversation is a milestone. The assistant still has to connect to real systems and behave reliably.
AI Agents & Assistants can move directly into the capabilities required to supply trusted context, tool access, infrastructure and production oversight around the agent.
FAQ
Common questions about AI Agents & Assistants.
Assistants help users; agents can also act across tools and systems. An assistant primarily helps a user understand, create or decide through interaction. An agent generally has greater ability to plan steps, use tools and take actions across connected systems. In practice, many useful systems combine both behaviours rather than fitting neatly into one category.
A conventional chatbot often answers from predefined flows or limited information. A modern AI assistant can use natural language, retrieve contextual knowledge and interact with systems or tools behind the conversation.
Yes. Agents can call APIs and interact with authorised systems where the use case justifies it. We define exactly which actions are permitted and where approval is required.
Yes. It can be connected to controlled documents and knowledge sources through retrieval and access-aware architectures.
Yes. Context and retrieval can respect user identity, role, organisation and permissions so the assistant does not expose information simply because it exists in the knowledge base.
It can, where persistent context is useful and appropriate. Memory needs to be deliberately designed around relevance, privacy, user expectations and data retention rather than enabled indiscriminately.
Yes. An agent can use multiple APIs or tools in one workflow, but system ownership, permissions, sequencing and failure handling need to be made explicit.
Only as much as the workflow safely benefits from. Low-risk actions can sometimes be automated fully, while consequential actions may require explicit confirmation or human review.
The system should be designed for uncertainty. Depending on the workflow, this can include validation, retries, constrained tools, fallback behaviour, escalation and human approval.
We test realistic conversations, retrieval quality, tool selection, action sequences, edge cases, access control and failure scenarios rather than evaluating only the quality of individual responses.
Yes. Assistants can be embedded into existing SaaS, web, mobile and internal applications where identity, context and system integrations support the experience.
Yes. We can continue with AI engineering, system integration, data engineering, cloud operations, observability and ongoing agent evaluation.
