Agents that reason, act and ask.
We build LLM agents that read unstructured requests, look up policy, call enterprise systems and hand decisions to people when the stakes call for it.
Rules run out where judgement starts.
Classic RPA needs structured inputs and fixed rules. A large share of operational work has neither. A customer email mixes a complaint, a refund request and an address change. A ticket describes a symptom, and the fix depends on a policy buried in a forty-page SOP.
LLM agents close that gap. They interpret the request, retrieve the relevant policy, plan the steps and call tools such as APIs, bots or database queries to act. Engineered properly, they stay inside defined permissions, cite their sources, and pause for human approval before anything irreversible.
Retrieval over your policies and SOPs keeps responses tied to approved sources.
Agents can only call tools you expose, with scoped credentials and parameter validation.
Evaluation harnesses score every prompt and model change before it reaches production.
Approval steps sit in front of payments, refunds, master data changes and outbound messages.
Production agents, with the controls.
Email and ticket understanding
Intent detection, entity extraction and multi-request splitting for shared mailboxes, ITSM queues and customer service channels.
01RAG over policies and SOPs
Chunking, embedding and retrieval pipelines over manuals, policies and knowledge bases, with citations in every answer.
02Tool-calling agents
Agents that query ERPs, CRMs and ticketing systems through typed functions with schema-checked inputs and outputs.
03Agents orchestrating bots
An agent decides what needs doing and triggers existing RPA bots or API flows to execute deterministic steps.
04Guardrails
Input and output filters, PII redaction, prompt-injection defences, topic restrictions and action allow-lists.
05Evaluation and monitoring
Golden test sets, automated scoring, regression checks on model or prompt changes, and production traces for review.
06Narrow first, then widen.
Choose a bounded use case
We start where inputs are messy, actions are well defined and an error is recoverable, such as triaging inbound requests or drafting responses for review.
Build the evaluation set
Before any prompt is written we assemble real examples with expected outcomes. This set decides whether the agent is ready.
Design tools and permissions
Each system action becomes a narrow, typed tool with its own service account. Write actions get approval gates and idempotency keys.
Iterate against the harness
Prompts, retrieval settings and model choices are versioned and compared on the evaluation set. Changes ship only when scores hold or improve.
Release with oversight
We launch in shadow or review mode, track override rates and cost per task, and widen autonomy only where the data supports it.
Models, orchestration and controls.
Models
Orchestration
Retrieval
Governance
Where agents typically earn their keep.
Request handling
Inbound emails and tickets are read, classified and actioned or drafted within minutes.
Automation coverage
Unstructured work that rules-based bots could never touch becomes automatable.
Policy answers
Responses reference the SOP section they came from, so reviewers can verify quickly.
Risk
Irreversible actions always pass through a named approver with the full context.
How do you stop an agent from hallucinating?
We ground it with retrieval, require citations, constrain outputs to schemas and validate tool parameters. The evaluation harness measures factual accuracy on real cases, and low-confidence results go to a person.
Can agents work with our existing RPA bots?
Yes. A common pattern has the agent interpret the request and decide the path, then call an existing bot or API flow to perform the deterministic steps. You keep the reliability of the bot and add judgement in front of it.
Which model do you recommend?
It depends on task difficulty, latency, cost and data residency. We usually test two or three models against your evaluation set and pick on measured results. The design keeps the model swappable.
How do you handle prompt injection?
We treat retrieved content and user input as untrusted, separate instructions from data, restrict tools to an allow-list and require approval for sensitive actions. Injection tests are part of the evaluation set.
Is our data used to train public models?
We use enterprise API terms or private deployments that exclude your data from provider training, and we can deploy in your own cloud tenancy and region.
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