5 days → 30 mins. Redesigning Caribou's client onboarding from a manual task list to an AI-first data-dump flow

Caribou already disrupted the old way — replacing the 2–3 weeks of functional interviews that traditional consultancies ran with a structured digital process. But even the product's own task-list approach still took customers 4–5 days of work. I designed the flow that collapsed that to under an hour.

The Problem

Before Caribou existed, onboarding a new client meant the traditional consultancy playbook: weeks of functional interviews across multiple stakeholders, manually extracting entity details, financials, contracts, people data, and more. For a typical organisation with 3 entities, that process took 2–3 weeks. Not because the information was complex — because the process of getting it out of people was slow, fragmented, and entirely manual.

Caribou's product replaced that with a structured task-list approach, which was a genuine step forward — it brought the timeline down to 4–5 days. But 4–5 days is still a lot of work when you're filling in 8–12 forms covering 100+ fields, and the information already exists somewhere in documents the customer has on hand.

The data wasn't missing. It was just trapped — in scattered files, spreadsheets, and people's heads. The task list gave it structure, but customers were still doing all the extraction themselves, field by field.

What I did

Identified the data-dump flow as the single highest-leverage onboarding problem. Caribou had already solved the "replace the consultancy" part. The remaining bottleneck wasn't "customers don't have the information" — it was "we're still making them type it all in manually."

Proposed a fundamentally different approach: let customers upload their existing documents, process and classify them automatically, and present the extracted insights as one-click suggestions inside the same forms they'd otherwise fill by hand.

Scoped the MVP with engineering. The full vision was ambitious — the shippable version needed to be useful from day one without waiting for perfect extraction accuracy. We designed for graceful degradation: if the AI was confident, one click. If it wasn't, the field was still there for manual entry. No dead ends.

Designed the end-to-end flow:

1. Upload: Customer uploads their existing documents (financials, contracts, org charts, whatever they have)

2. Process & classify: The system ingests, parses, and classifies the content, mapping it to the relevant forms and fields

3. Form-by-form review: Customer moves through each form (entity details, accounting, people, contracts, production, etc.). In each field, processed insights are surfaced and available to accept with one click

4. Confirm without skipping:The flow ensures every key field is addressed. The user could accepted from a suggestion, edit an accepted suggestion, or manually enter from scratch.

Shipped the flow to customers (May 2026), replacing the task-list approach entirely. The new analytics in place include time on task and time to completion of flow. Additional tracking for acceptance rate for AI suggestions.

Design decisions that mattered

One-click accept, not auto-fill. It would have been easier to just dump the extracted data into every field automatically. But trust matters. When you're entering financials and contract details, you want to see what the system found and say "yes, that's right", and not discover later that it guessed wrong! The one-click pattern gave users control without giving them work.

Form-by-form, not all-at-once. 100+ fields on one screen would be overwhelming. Breaking it into logical forms (entity details, then financials, then people, then contracts) meant each step felt manageable. You're never staring at the whole mountain, just the next stretch. This also made it scalable to larger organisations, where user roles come into play… where Hannah from HR could own all tasks related to people data, while Bill from Accounting filled in the financials.

Graceful degradation. The AI extraction wasn't perfect. Some fields would have high-confidence suggestions, some would have low-confidence ones, some would have nothing. The design handled all the different states without breaking the flow. Only suggestions with a high level of confidence were shown, to build trust and prevent noise. Manual entry was always an option.

Confirmation without skipping. The old task-list approach at least had one advantage: every field was explicitly required. The new flow needed the same discipline. Every required field had to be explicitly addressed before moving on, either by accepting a suggestion as is, editing it, or typing something in.

The Outcome

Onboarding time:Traditional consultancy method took 2–3 weeks → Caribou's task-list approach brought it to 4–5 days → The data-dump flow I designed brought it to 30–45 minutes (even filled entirely manually, ~1 day — still a 5x improvement over the previous product experience)

Forms covered: 8–12 per typical engagement, 100+ fields total

Tracking in progress (PostHog instrumentation live May 2026):

• Field acceptance rate (% AI suggestions accepted)

• Funnel completion rate (% completion)

• Customer count through new flow