Services/Generative AI
Agentic AI
Systems that take an objective, plan the steps, use tools, and complete multi-step work with minimal supervision.
- 8 to 16 weeks
- Fixed scope, or an ongoing team
- Architect plus agent engineers
- Complex processes that need judgement at several steps
What we do
Agentic systems go beyond question answering. They decompose a goal, act through tools and APIs, check their own results, and recover from errors. We build them with the planning, memory, and oversight layers that make that autonomy safe to run.
What the work includes
Planning and decomposition
Agents that turn a high-level goal into sub-tasks, sequence them, and re-plan when intermediate results change the picture.
Tool use and function calling
APIs, databases, search, and internal tools exposed as functions the agent can call, with permissions scoped per tool.
Self-checking and recovery
Agents that evaluate their own output, detect failures, and retry with a different approach before escalating to a person.
Persistent memory
Short-term working state and long-term knowledge, so agents improve with use rather than starting from zero each run.
Case study
A natural-language fault-reporting agent for property management
A ten-step reporting workflow replaced by conversation, with structured fields extracted, validated, and filed to the client CMMS.
Read the case studyHow an engagement runs
Scope, build, evaluate, operate.
The same four stages on every engagement, so you always know what happens next and what you will have at the end of it.
Scope
A short discovery with the people who own the problem. Output: a written scope, acceptance criteria, and a fixed estimate.
Build
A named lead and a team sized to the scope. Working software from the first weeks, demonstrated on a fixed cadence.
Evaluate
Every AI component is measured against real cases before rollout. The numbers decide when it goes live.
Operate
Deployment, monitoring, and a support window. Then a handover, or an ongoing team if you want one.
Typical stack
Tools we commonly use for this work. The final choice follows your requirements, region, and existing platform.
- Python
- LangGraph
- LangChain
- OpenAI
- Anthropic
- FastAPI
- Redis
- Neo4j
- PostgreSQL
Related use cases
Related services.
Conversational AI
Assistants that hold context, answer from your knowledge, and hand off to a person at the right moment.
Enterprise RAG
Retrieval-augmented systems that answer questions from your documents and data, with citations and access control.
AI-Powered Search
Search that understands intent, not only keywords, across large content libraries and product catalogues.
Agentic systems
Autonomy with a clear boundary
The useful question is not how autonomous an agent can be, but where the boundary of its authority sits. We design that boundary explicitly and build the system around it.