The interesting work in your organisation is not a single question and a single answer. It is a sequence — read this, retrieve that, decide, call a tool, validate, escalate, log. Agentic workflows are how we build AI that does that work in production.
A prompt produces text. A workflow produces an outcome. The difference is the layer between them — agents, tools, data, logic, validation and human review, designed to act inside your systems and be accountable for the result.
One model. One context window. One response. Useful for assistance and exploration — not for running a process. The user is responsible for everything before and after the answer.
Specialised agents plan and decide. Tools read and write to real systems. Validation gates catch mistakes before they propagate. People review what matters. The output is work completed.
Six ingredients. Every workflow we ship is built out of these — varied in proportion, combined for the job. Anything sold as "AI" that does not include them is a feature demo.
Each agent is purpose-built for one role — triage, retrieval, validation, drafting, action. They reason, plan and hand off cleanly. None of them pretends to do everything.
Agents call your real systems — CRM, ERP, ticket platform, billing, internal APIs, document store. Output is work performed inside your stack, not a chat reply to be re-typed somewhere.
Hybrid retrieval — vector plus structured queries — scoped to what each workflow actually needs. Data residency, access policy and PII handling are part of the design, not a bolt-on.
Triggers, branches, retries, idempotency, fallbacks. We treat an agent run like a real distributed system — observable end-to-end and recoverable when something inevitably goes wrong.
Evaluation suites, schema checks, programmatic guards, regression tests. Every change to a production workflow is measured against a fixed dataset before it ships — and the metric is visible.
Reviewer queues, approval gates, intervention surfaces — wherever the decision matters or the risk is non-trivial. People stay in command of the system, with audit, override and explanation built in.
These are the workflows where, in our experience, agentic systems clear their own cost inside a quarter. Each one is concrete: defined input, defined output, owned by a named team inside the customer organisation.
Read large volumes of contracts, invoices, claims, certifications or technical drawings. Extract structured data, summaries and decisions. Flag exceptions. Route to the right person.
Retrieval-grounded answers from your own corpus: wiki, ticketing, document stores, code, policies. Every answer is cited and linked. The assistant declines when sources don't support a claim.
Scheduled pulls from operational systems, reconciliation across sources, draft analysis, change-vs-prior, anomaly callouts — handed to a human reviewer for sign-off before distribution.
Pre-meeting briefings, account intelligence and technographic enrichment. Public sources + your CRM history, scored against your ideal customer profile, with a next-best-action the rep can actually use.
Triage incoming tickets, resolve the deterministic ones, draft replies on the rest, route with full context to the right human. The system learns the categories it should never act on alone.
Continuous evidence collection, control mapping and gap detection. Policies, logs, configurations and vendor documents are pulled, classified and held against the framework you operate under — SOC 2, ISO 27001, DORA, the EU AI Act.
Aggregate signals from operational systems, model the option space, brief the decision-maker. The system surfaces evidence, weighs trade-offs and notes its own uncertainty. It is not the decider — and is explicit about that.
Each step has a defined output. You see something working before the end of week three, and you can stop after any step — what's been built is yours.
We map the workflow down to the task level, define the spec and the eval set, and agree the boundary of the first module.
Agent topology, tools, data scope, validation gates, human-review points. We commit to architecture before code.
Spec-driven engineering. Continuous evaluation against the held-out set. One production-grade module per two-week cycle.
Live deploy with observability. Source, infra-as-code, eval suite and runbooks handed to your team — they own it from here.