Employee & service copilots
Help teams locate relevant information, prepare responses and navigate procedures. Embed the assistant in the workflow with clear sources, user context and a handoff when the information is incomplete.
Generative & agentic AIAI that assists.
AI that assists.
Workflows that act.
Generative and agentic AI development for enterprise work. Build copilots, document intelligence and controlled workflows that connect useful context with the ability to assist or take an authorized action.
Generative & agentic AIFrom impressive answers
From impressive answers
to useful experiences.
A compelling demonstration is a starting point. A useful enterprise application also needs reliable information, a clear task, an intuitive experience and a defined boundary between assistance and action.
We develop GenAI experiences around those requirements. The work can include knowledge assistants, document processing, structured information extraction and human-reviewed drafting. Where agents are appropriate, we design a bounded workflow with explicit permissions, approved tools and a path for escalation.
The objective is to improve a specific activity: reducing search effort, accelerating a document review or helping an employee complete a task with better context. We agree how that improvement will be assessed before expanding the solution.
Applications
Build around a meaningful task.
The examples below illustrate possible engagement areas. Each initiative is scoped to your organization’s requirements.
Document intelligence
Extract structured fields, classify documents and produce reviewable summaries. Include validation rules and exception handling so uncertain outputs receive the right attention.
Agentic workflows & automation
Coordinate bounded tasks across approved tools and APIs. Define the allowed actions, validation rules, approval points and fallback paths before a workflow can change a business system.
The engineering approachMake the foundations
Make the foundations
part of the product.
Workflow architecture
Map inputs, outputs, user decisions and system actions. Separate tasks that need generation from tasks that are better handled through rules, search or conventional software.
Grounding & context
Determine what information the model needs, where it comes from and which users may access it. Combine prompts with retrieval when the use case requires enterprise knowledge.
Evaluation & safeguards
Test task completion, unsupported statements, prompt injection attempts and failure handling. Define when the application should ask for clarification or decline to proceed.
Integration & operations
Connect identity, APIs and user interfaces. Capture useful telemetry without unnecessary sensitive data and establish a process for reviewing model or prompt changes.
Define successMeasure what matters
Measure what matters
in your environment.
A useful evaluation asks whether the application can perform the task under realistic conditions. We help establish acceptance criteria and make trade-offs visible before a broader release.
Example application: an operations knowledge assistant
An employee asks how to handle an exception. The application retrieves approved procedures, explains the relevant steps and cites its sources. If the request would change a business record, the system routes that action through an authorized workflow with any required approval. The assistant supports the process while the underlying permissions remain in force.
Illustrative application, not a client case study.
Task quality
Does the output meet the requirements of the actual business task?
Review effort
How much checking or correction does a user need before the result is useful?
User adoption
Do people return to the capability and successfully complete their workflows?
Latency & unit cost
Can the experience meet response-time and cost constraints at expected usage?
The people behind the solutionAdd the expertise
Add the expertise
your team needs.
Already leading the initiative internally? We can focus the engagement on recruiting the specialists who complement your existing team.
Explore specialist staffing- GenAI solution architect
- LLM application engineer
- Data engineer
- Full-stack developer
- AI evaluation specialist
One initiative. The right engagement.
Discuss an individual placement, contract expertise or a scoped development effort. Responsibilities, deliverables and working arrangements are agreed before the engagement begins.
Common questions
Go deeper into Generative & agentic AI.
Where should a GenAI initiative begin?
Begin with a repeatable task, a clear user group and accessible information. Establish the current process and a measurable definition of improvement before choosing the model or application architecture.
Does every use case need an autonomous agent?
No. Search, a structured workflow or an assistant with human review may be more appropriate. Agentic behavior should be introduced only when the task benefits from controlled tool use and the boundaries can be enforced.
Can GenAI be added to an existing application?
Yes. An engagement can focus on a specific feature, API or workflow within an existing product. Integration requirements, identity, data access and operational ownership are part of the scope.
Let’s define what useful AI looks like for you.
Start with a business task, a data challenge or a specialist role. We’ll help shape the next step.