Most AI automation projects break in production. The fix is in how you build.
The pattern is familiar. A workflow that worked in the demo struggles with edge cases. The error rate creeps up over weeks and nobody is watching. The agent is too autonomous in places it should not be, and not autonomous enough in places it could move faster. The handoff to operations was an afterthought.
AI automation that holds up in production looks different. Bounded autonomy with audit trails. Human-in-the-loop checkpoints at the steps where stakes are real. Deterministic fallbacks when the model fails. Monitoring tied to operational metrics that matter to the business. Integration with the systems your team already uses, not a parallel stack to maintain.
That is the model we build around. AI automation engagements that ship working automations with the operational layer to run them, not slide decks recommending what to automate.
AI-driven workflows across operations
Production-grade automation patterns we have built before, ready to be shaped to your operation.
Support and customer ops
Inbound triage, deflection, draft response, escalation routing, knowledge retrieval. Designed to take repetitive load off the team without blocking edge cases.
Back-office and finance ops
Document understanding, structured extraction, reconciliation, anomaly review, supplier onboarding. Audit trails and reviewer queues for the steps that need them.
Internal tools and copilots
AI assistants embedded in the tools your team already uses. Search, summarisation, drafting, and lookup against internal systems. Built to feel native, not bolted on.
Knowledge and content workflows
Knowledge base curation, content generation with brand and tone control, internal documentation copilots, automated tagging and classification.
Bounded agents
Multi-step agents that complete tasks across systems with audit trails and deterministic fallbacks. Autonomy where it is safe; review checkpoints where it matters.
Integration and orchestration
Wiring AI into your existing stack: Salesforce, HubSpot, Zendesk, internal databases, custom APIs. We integrate with what you have, not what we wish you had.
Discovery, design, build, roll out
Productized phases with named deliverables. The same shape across every automation engagement so you know what to expect.
One to two weeks. Workflow mapping, automation candidate scoring, data and integration assessment, autonomy boundaries, scoped rollout plan. You finish with a clear list of what to automate and in what order.
Four to eight weeks. Working automation built and integrated with your systems. Eval harness, audit trail, reviewer queues where needed, fallback paths. Weekly demos against real workflow data.
Two to four weeks. Phased rollout against the operations team, monitoring calibration, alert thresholds, runbook walk-through, baseline measurement against the eval harness.
Full handover with documented architecture and runbooks, or a retainer where we build the next automation in the queue while your team learns the patterns.
Production-grade automation, not a recommendation
Every engagement ends with a working automation in production and the artifacts your team needs to run it.
Automation that survives production, not a workshop output
AI-first delivery, productized engagements, and a team built around the patterns that actually hold up in operations.
Bounded autonomy by design
We design autonomy at the step level. Where it is safe, the automation moves fast. Where stakes are real, a human reviews. The boundaries are explicit, auditable, and easy to adjust as confidence grows.
Built into your stack, not parallel to it
We integrate with the tools your operations team already uses. Salesforce, HubSpot, Zendesk, internal databases, custom APIs. The automation should feel like a capability inside your operation, not a separate system to maintain.
Eval and monitoring from week one
Evaluation harness tied to operational KPIs exists before the first automation runs. Monitoring is wired before rollout. Drift detection and reviewer feedback loops are designed in.
Productized scope, transparent partnership
Discovery Sprint, Automation Build, Rollout, and ongoing iteration. Named deliverables, week-by-week cadence, exit ramps. You see what you get and when before the work starts.
Where to look next
Deeper detail on the engagements and approach behind this work.
Common questions
What is AI automation consulting?
AI automation consulting is the work of identifying which workflows in an operation can be automated with AI, designing how the automation should work (autonomy boundaries, audit trails, human checkpoints, fallbacks), and building it into production. Done well, it ends with a working automation your team can run, not a slide deck recommending what to automate.
How is this different from RPA or low-code automation tools?
Traditional RPA and low-code automation work well for deterministic workflows where every step is defined. AI automation is for workflows that involve language, judgement, classification, or multi-step reasoning. The two often complement each other. We use the right tool for each step rather than forcing AI where deterministic logic is cleaner.
Will the automation replace our team?
Almost never. Most automations we build take repetitive load off a team so they can spend their time on higher-value work, with human checkpoints at the steps where judgement matters. The framing we use is augmentation by default, automation only where it is genuinely safe.
How do we know what to automate first?
The Discovery Sprint maps your workflows, scores automation candidates against value, complexity, and risk, and produces a phased rollout plan. You finish with a clear list of what to automate, in what order, and why.
What about audit trails and compliance?
Audit trails are part of every automation we build. Each automated decision is logged with the inputs, the model output, and the outcome. Reviewer queues sit on top of the audit trail for the steps that need human review. Compliance posture is shaped to your jurisdiction during Discovery.
Do you publish pricing?
No. Every automation engagement is bespoke and the scope drives the cost. Our packages publish week-by-week cadence, named deliverables, and exit ramps so you can compare scope transparently. Pricing is set per project against the agreed scope.
Ready to automate operations with AI that holds up in production?
Book a free 15-minute feasibility triage. We will scope what your automation looks like, map the rollout, and give you an honest read on timing and risk.