Service · 02
AI for the steps a workflow can't decide.
Rule-based automation handles anything you can draw as a decision tree. It falls over the moment a step needs judgment — reading a contract, classifying a ticket, researching an account. That's the gap we fill, and we're equally direct about where AI is still the wrong tool.
The problem we solve
AI pilots demo beautifully and die in production.
The failure mode isn't that the model breaks. It's that it's confidently wrong and nothing downstream notices.
"The board is asking about AI and we don't have an answer."
Vendors are pitching, your team is experimenting, and nobody can point to a use case where AI does something worth paying for in your operation specifically.
The plausible-but-wrong output
An extraction misreads a total. A classifier routes a churn-risk ticket to billing. Rule-based automation either works or visibly breaks; AI quietly produces something that looks right and gets written to your CRM.
Pilots that never reach production
The prototype works on ten examples. Then nobody has built the validation, the fallback path, the human gate, or the logging — so it never earns the trust to run unsupervised.
What we build
AI steps you can actually put in front of a customer.
Every build below is a hybrid: deterministic workflow owns routing, state, and error handling; the model owns exactly one judgment step; a schema validates the handoff.
Document extraction with validation
Contracts, invoices, intake forms, and scanned PDFs turned into structured data — then checked before anything writes. Totals must sum, dates must parse, vendors must match your master list, or it routes to review.
OutcomeContract → payment schedule + milestones, in the quote-to-cash build
AI agents for research and drafting
Pre-call account research, personalised outbound drafts, renewal-email first passes. The agent proposes; a human approves before anything leaves the building.
OutcomeTypical run cost: $400–$800/month at ~1,500 records
Classification & triage
Support tickets, inbound leads, and exception queues sorted by intent and urgency, with confidence thresholds that route the uncertain cases to a person instead of guessing.
AI opportunity assessment
Before any build: we audit where your team does repetitive judgment work, evaluate buy-vs-build against the current vendor landscape, and hand back use cases ranked by impact with honest build estimates.
OutcomeCommon output: 3–5 pilots ranked, plus what to skip
Guardrails for an existing AI workflow
Already shipped something that's misfiring? We add the structured-output enforcement, confidence routing, human-in-the-loop gates, and per-invocation logging it should have had.
Real example
AI contract reading inside a quote-to-cash build — 11 days to 38 hours.
The setup
A 60-person consulting firm's invoicing waited on a human reading each signed contract for payment terms. We put an AI extraction step in the middle of the workflow rather than rebuilding the process around the model.
What we did
- 1Closed-Won in Salesforce drops the signed contract into the pipeline
- 2AI step extracts payment schedule, line items, and milestone dates
- 3Schema validation: totals reconcile against the deal amount or it stops
- 4Validated terms create a scheduled invoice plan in Stripe
- 5Anything ambiguous routes to a human queue with the source PDF attached
- 6Two flagged exceptions in three months — both genuine contract ambiguities, not model errors
Outcome
- Time-to-invoice
- 11 days → 38 hrs
- Manual entry eliminated
- 82%
- Invoice errors
- ≈ zero
- Payback period
- 11 weeks
How we deliver
Prove it on your data, then gate it.
We never ship an AI step straight to production. It gets tested against your historical records first, then runs behind a human gate until the numbers earn the gate's removal.
Use-case selection (week 1)
Which step actually needs judgment, and what a wrong answer costs there. Anything cheap, high-volume, and rule-shaped goes back to deterministic automation — that's most of it.
Backtest on your records (week 2)
We run the step against a sample of your historical documents or tickets and measure agreement with what your team actually decided. If it can't beat the bar, we tell you before you spend build budget.
Build with gates, then loosen
Ship with validation, confidence routing, and human approval on anything irreversible. Once the log shows it's reliable, we relax the gates you choose to relax — never the ones touching money or customers.
Tools we work in
We build inside your stack — not on top of it.
Don't see a tool you use? Ask. We've worked with 100+ platforms; the list below is what comes up most often.
- Claude API
- OpenAI API
- n8n AI nodes
- LangChain
- HubSpot Breeze
- Salesforce Agentforce
- Salesforce
- Slack
- Google Drive
- Stripe
Pricing snapshot
$2,500 – $25,000
1–5 weeks typical
A single validated AI step starts at $2,500. Multi-step agents with tool access run higher. Add $400–$800/month in API and tool cost for a mid-volume workflow — a fraction of the labour it replaces, and we'll size it before you commit.
- Free 30-min scoping call
- Written fixed-fee quote
- Source code + workflows you own
- 30 days post-launch tuning
- No hourly surprises
Questions
Common questions.
- Will AI agents replace our SDRs, CSMs, or ops team?
- No, and anyone promising that is selling marketing. AI replaces the research and drafting time inside a person's day, not the judgment, relationships, or follow-through. The teams that win use it so a smaller human team can cover more ground.
- How do you stop it from making things up?
- Structured output enforced by a JSON schema, validated before anything writes downstream; confidence thresholds that route uncertain cases to a human; approval gates on irreversible actions; and logging of every invocation. The model proposes, the schema disposes.
- Which models do you build on?
- Claude and GPT via API for custom work, plus the CRM-native options (HubSpot Breeze, Salesforce Agentforce) where they genuinely fit. We pick per task — higher-capability models where writing quality matters, cheaper ones for classification.
- Can our data be used to train someone's model?
- Not in how we build it. We use API tiers that exclude training on your data, and for regulated clients the workflow engine runs self-hosted inside your own infrastructure so the only thing leaving is the specific prompt payload you've approved.
- What if AI turns out to be the wrong answer for our use case?
- Then we say so and build the deterministic version instead — that's the most common outcome of an opportunity assessment. Our AI automation guide is explicit about where agents still don't earn their cost.
Explore more
Other services we offer.
01
Workflow Automation & Integrations
The core build: multi-step workflows that move data across your CRM, billing, project management, and comms — automatically, reliably, and at scale.
Learn more03
Automation Strategy & Program Leadership
The engagement where we think and lead rather than build: audit, prioritisation, and PMP-certified program leadership so the right things get automated in the right order.
Learn more04
Platform Implementations
End-to-end configuration, data migration, go-live, and team enablement for the platforms your business runs on.
Learn more05
n8n Consulting & Development
The platform behind most of our builds: self-hosted deployments, Zapier and Make migrations, and managed hosting — plus n8n-native workflow development.
Learn moreGo deeper
AI Automation & Agents playbooks from the blog.
AI Agents vs Workflow Automation: When to Use Each (and When to Combine Them)
Forrester predicts AI agents will sit inside a third of B2B payment workflows by year-end. Here's the practical difference between agents and traditional automation, and the hybrid architecture we ship for clients.
Read the playbookHow to Build an AI Sales Agent with n8n and Claude in 2026
We've shipped this for clients several times now. Here's the architecture, the prompts, the guardrails — and the parts where AI agents replace SDRs cleanly versus where you still need a human in the loop.
Read the playbookBuilding an AI-Powered Customer Onboarding Workflow
Customer onboarding is the highest-leverage place to use AI in B2B operations. Here's the workflow we ship — contract analysis, kickoff personalization, and milestone tracking.
Read the playbookReady to scope your ai automation & agents project?
Request a call. We'll talk through your stack, your goals, and walk away with a scoped plan and a quote.