Systems that think, decide and act on your data.
Agents, knowledge and data engineered into one accountable operating layer. From retrieval and memory to orchestration, evaluation and governance, we build AI that leaves the chat window and does the work.
01Point of view
AI becomes valuable when it leaves the chat window and enters the operation. We engineer the whole intelligence loop, on data you can trust.
02AI system
Intelligence earns adoption at the point of interaction.
We design AI people can understand, direct and trust.
Useful intelligence starts with usable knowledge.
We turn fragmented information into retrievable operating context.
The right model is a system decision, not a popularity contest.
We engineer the intelligence layer around quality, speed, control and cost.
Every useful system remembers what matters.
We give AI the continuity to understand the user, the work and the moment.
Agents need a harness before they need autonomy.
We build the runtime that turns model capability into dependable execution.
One agent performs a task. A coordinated system moves the work.
We orchestrate specialised agents around a shared outcome and accountable handoffs.
Intelligence creates value when it can touch the real world.
We connect agents to the software, data and actions that businesses already depend on.
If we cannot observe it, we cannot improve or trust it.
We make AI quality visible from development through production.
Trust is architecture, not a policy document.
We engineer control, accountability and protection into every layer.
AI economics must work before AI ambition can scale.
We optimise the infrastructure so intelligence stays fast, resilient and commercially viable.
03Data foundation
Stage 01 · Ingest
Documents, tables, events and logs arrive from wherever they live. Batch and streaming pipelines land them in a lakehouse or warehouse with lineage from the first byte.
04Autonomy
AI answers. People act.
05Evaluation
✕ case 14 · refund over limit · approval skipped
✕ case 38 · citation missing · groundedness 0.61
▸ gate · 2 regressions · blocked from release
Task success by version
Tokens
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Tool calls
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Verdict
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We trace every step an agent takes, score runs against golden datasets, and gate releases on regressions. The same telemetry watches production for drift in quality, latency and cost.
06Capabilities
Harnesses that turn a model into a dependable worker: planning, state, checkpoints, retries and recovery, with typed tools and hard budgets on steps and spend.
You receive
Supervisors, specialists and handoffs designed around one shared outcome. Parallel where it pays, sequential where accountability matters.
Retrieval that finds the right fact: structured chunking, hybrid search, reranking, entity graphs and permission-aware context assembly.
The foundation under every agent: batch and streaming pipelines, lakehouse and warehouse models, data contracts and quality monitoring.
Eval suites built from your real tasks, tracing on every run and production monitors for quality, latency and cost per task.
Least-privilege tools, sandboxed execution, prompt-injection defence, data protection and an audit trail for every decision an agent makes.
07Stack
Representative technologies across the ten layers, not a fixed stack. We choose per problem, on quality, control and cost.
08FAQ
An agentic AI system is software in which AI models do more than answer questions: they plan a task, retrieve the context they need, call tools and APIs to act, check their own work and hand off to people or other agents. Asymmetri engineers these systems end to end, from data and knowledge to the agent runtime, orchestration, evaluation and governance, so the AI completes real work inside your operation instead of living in a chat window.
We build an evaluation suite from your real tasks before an agent ships, then run it as a regression gate on every change to prompts, models, tools or data. In production every run is traced, and we monitor task success, groundedness, latency and cost per task, with alerts when quality drifts. Tools such as Langfuse, LangSmith, Arize Phoenix, Braintrust and OpenTelemetry are representative of what we use.
No. Asymmetri is model-agnostic. We choose models per task on quality, speed, control and cost, and often route between several, including OpenAI, Anthropic Claude, Google Gemini and open-weight models served with tools like vLLM. Routing and evaluation are built in, so you can change models as the market moves without rebuilding the system.
Governance is designed into the architecture. Agents get least-privilege access to tools and data, risky actions run in sandboxes or wait for human approval, inputs are screened for prompt injection, personal data is detected and redacted, and every decision is logged for audit. Autonomy is raised one step at a time, from assist to copilot to agent, only as evaluations earn that trust.
Bring us the hard problem. We start by framing the valuable decision or workflow, then design the interaction and control model, engineer the agent and its data foundation, prove performance, safety and economics with evaluations, and operate it in the real workflow. Write to hey@asymmetri.in to begin.
Application Development↗
Products people transact with, depend on and trust.
