What does consultance.ai build?

Source-grounded AI deal desks and finance systems: diligence, reconciliation, financial close, reporting, underwriting, and compliance knowledge workflows — designed, deployed, and maintained end-to-end.

Who does consultance.ai work with?

Private equity funds, private credit teams, CFO offices, family offices, broker-dealers, and regulated finance teams that need faster, traceable workflows around confidential financial and deal data.

What makes a finance AI system trustworthy?

Material outputs need source links, verification states, approval controls, audit logging, and a clear exception path. The system automates the intelligence work while the finance or deal team retains judgment and final approval.

How long does an AI deal desk or finance automation build take?

A scoped proof of concept typically runs on representative data first. Production timing depends on data condition, integrations, deployment requirements, and the number of workflows in scope.

Do you transfer code and IP?

Yes. Code and IP transfer at every milestone. No subcontractors, no offshore handoffs — direct line to the engineers building.

Do clients own the system?

Yes. The engagement is designed around client-owned code, prompts, data, and deployment. The team can run the system independently or retain consultance.ai for support and further build work.

Services · Last updated Jun 2026

AI workflow automation for the work your team repeats every week.

We connect AI to the tools your team already lives in: inboxes, CRMs, sheets, databases, documents, calendars, dashboards. The system classifies, routes, drafts, summarizes, and updates so people are not the copy-paste layer.

What we build

Workflow mapping

AI classification and extraction

Routing and approval logic

CRM and inbox updates

Monitoring and exception handling

Why it matters

We do not build automation for the demo. We build it for the Monday morning when a real lead lands at 9pm on a Saturday and someone needs to respond before they go cold.

Cut repetitive manual work
Speed up operational handoffs
Make workflow status visible

The workflows that pay back first

After auditing dozens of operations, the same three candidates win almost every time. First, inbox-driven intake: leads, bookings, invoices, and support requests that arrive as unstructured email and wait for a human to read, classify, and re-type into another system. Second, document work: extracting fields from statements, contracts, and reports, then reconciling them against what another system says should be there. Third, recurring reporting: the same numbers assembled from the same sources every week, by hand.

A real example from our own operations: payout reconciliation for a short-let property business. Every Booking.com and Airbnb payout email is pulled automatically, parsed, and matched against the booking ledger; mismatches are queued as exceptions with a money-at-risk total attached. The first full run surfaced tens of thousands of pounds in payout discrepancies a human review had missed. That workflow now runs unattended, and a person only sees the exceptions.

How we build it so it survives contact with Monday

Every automation we ship has the same skeleton: a deterministic backbone with AI only at the judgment steps. Classification, extraction, and drafting are model calls. Routing, retries, logging, and the audit trail are plain code. That split is why our systems keep working when a model has a bad day. The AI step fails loudly into an exception queue instead of silently corrupting your CRM.

Anything that sends money or messages out of the building gets a human approval step on day one. Autonomy is earned with an error-rate track record, not assumed. And every workflow ships with monitoring: you see what ran, what it decided, and what it refused to decide.

If you want to feel the difference before paying for it, our free build library includes runnable versions of intake triage, reporting, and reconciliation workflows. Run one against your own data this weekend; the engagement is making it production-grade.

Questions about ai workflow automation

What workflows can AI automate?

Intake, classification, document extraction, summaries, lead routing, CRM updates, support triage, report drafting, and follow-up reminders.

Does it need custom software?

Sometimes. We pick the simplest reliable approach. Lightweight integrations when they work. Custom code when the workflow demands it.

AI workflow automation vs Zapier: which one?

Trigger-action tools are right for simple, deterministic hops between two apps. The moment a step needs judgment, like reading a document, deciding what kind it is, and drafting the reply, you need an AI layer. We compare the two honestly, costs and failure modes included, in our AI agents vs Zapier guide.

How long does a workflow automation project take?

A single workflow typically ships in two to four weeks: one week of mapping, one to two of build, the rest monitoring against real traffic with a human approving outputs until error rates earn autonomy.

What does workflow automation consulting cost?

Single-workflow builds usually land in the five-to-fifteen-thousand-dollar range; multi-system automations more. Fixed scope, agreed up front. The honest comparison point is the loaded cost of the hours the workflow currently burns every month.

Try it free before you hire us

These free builds from our AI build library show exactly how we approach ai workflow automation. Run one yourself this weekend — when you want it wired into your real stack, that is the engagement.

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