Agentic workflows
Multi-step agents with tool access, approval gates and full traces so every action is auditable and reversible.
AI Automation
AI Automation. Agents and workflows that do the work, not demos.
We automate the operational work that eats your team's week — with evaluation, guardrails and a human in the loop where it matters.
Overview
Most AI pilots stall because nobody designed the boring parts: data access, evaluation, failure handling and ownership. We start from a process you can measure — tickets closed, invoices processed, leads qualified — and automate it with retrieval, agents and deterministic fallbacks. Then we prove the accuracy before it touches a customer.
Typical outcomes
4 wks
Pilot to measurable result
65%
Support ticket deflection
11x
Documents processed per hour
72% — Average manual workload removed
Capabilities
What ai automation covers with us.
Pick the parts you need. We will tell you honestly which ones you do not.
Document & data processing
Extraction and classification pipelines for invoices, contracts, claims and onboarding packs with confidence scoring.
Support & sales automation
Deflection assistants, response drafting, CRM hygiene and lead qualification wired into the tools your team already uses.
RAG over your knowledge
Chunking, hybrid retrieval, reranking and citation-backed answers over documents, wikis and databases.
Evaluation & observability
Golden datasets, regression suites, drift alerts and cost-per-task dashboards — the difference between a pilot and production.
Guardrails & governance
PII redaction, prompt-injection defence, model routing, spend caps and policy controls documented for your risk team.
Deliverables
Exactly what lands in your hands.
Every engagement ends with artefacts your team can use without us in the room.
- Automation opportunity map with ROI per process
- Working pilot on your real data within four weeks
- Evaluation harness with accuracy and cost baselines
- Production deployment with monitoring and alerting
- Human-in-the-loop review interface
- Operating playbook and team training
Technology we use
Models
OpenAIAnthropicGoogle GeminiLlamaMistralWhisper
Orchestration
LangGraphLlamaIndexTemporaln8nCeleryInngest
Retrieval
pgvectorPineconeWeaviateQdrantElasticsearch
Ops
LangSmithLangfuseRagasPrometheusGrafana
Process
How a typical engagement runs.
Timelines vary with scope, but the sequence and the checkpoints do not.
Process audit
We shadow the workflow, measure current cost per task and rank candidates by value over effort.
Pilot on real data
A narrow slice in production conditions, scored against a golden dataset your team signs off on.
Harden
Guardrails, retries, fallbacks, cost caps and escalation paths for the cases the model should not own.
Roll out
Staged deployment by team or queue, with side-by-side human review until accuracy holds.
Operate
Monthly evaluation reports, model upgrades and expansion into the next process.
FAQ
AI Automation questions.
Will our data be used to train models?
No. We use enterprise API tiers with training opt-out, or self-hosted open models when your policy requires data never to leave your infrastructure.
What if the model gets it wrong?
Every automation ships with a confidence threshold, a human review queue for low-confidence cases and a deterministic fallback path. We design for graceful failure before we optimise accuracy.
How do you prove it is actually working?
We agree the metric before building — cost per task, deflection rate, cycle time — and report it against the pre-automation baseline every month.
Pairs well with
Services teams usually combine with this.
Next step
Need ai automation? Let us scope it.
Send a short brief and a senior specialist will reply within one business day with questions, an approach and an honest cost range.