Out of the pilot, into production, onto the P&L

Too big for a tool. Not big enough for the firms built around enterprise.

In 2026 the frontier labs launched multi-billion-dollar deployment firms aimed at large enterprises. At the other end, self-serve AI tools sell to individuals for a few dollars a ticket. Neither one will scope, build and run a system for a 300-person company. That is the engagement we built Victoria around.

Mid-market buyers want a phased engagement, a named pilot, real integration work, and someone accountable for running it afterwards. Early adopters who buy this way choose multi-agent solutions 82% of the time, and 59% of them expect cost savings above 20%.

Deloitte, 2026

Five phases. Testing is one of them.

• 01 Scope & data readiness

  One workflow, one owner, one number we agree to move. We check your data can carry it before anything is built; about half of AI projects need data work first.

• 02 Build against your systems

  Results come from how deeply the system is wired into your tools, not from which model it uses. One support team went from 32% to 77% of cases resolved by adding 25 integrations. We build and own that layer.

• 03 Test on your real cases

  Before anyone outside your team sees it, we run it against 100 to 300 of your real cases and agree the pass mark in advance. You sign off on results, not a demo.

• 04 Deploy with controls

  People stay in the loop wherever an error would cost real money. Spend limits, retry limits and a kill switch from day one.

• 05 Run & improve

  We operate what we build, measure it every month and improve it. Systems nobody maintains repeat their mistakes; MIT's researchers named that as the root cause of failed pilots.

Sources: Accenture; Lorikeet resolution benchmarks 2026; MIT Project NANDA

A working pilot in production by day 30.

• Wk 1 Scope & readiness

  We pick the workflow together, name the owner, agree the number, and check the data and access we need.

• Wk 2–3 Build

  We build against your systems, with a weekly walkthrough on real cases so nothing is a surprise at the end.

• Wk 4 Test & go live

  We run the acceptance set, hit the agreed pass mark, and switch it on with controls and a kill switch.

• After Run

  A monthly report, continuous improvement, and a person who answers.

What we need from you: one owner, access to the systems the workflow touches, and fifty or so real examples of the work.

Five things we build. Ranked by the evidence that they work.

• 01 Data & systems readiness

  Seven in ten companies your size say at least half of their core applications cannot support AI.

• 02 Customer support automation

  The only AI use case with a peer-reviewed causal result: +14% productivity across 5,179 support agents, +34% for the newest hires.

• 03 Document & back-office processing

  Contracts, invoices, claims, policies, onboarding packs, the month-end close.

• 04 Sales & marketing AI

  Marketing and customer success are the third- and fourth-largest AI budgets in the enterprise, and the most crowded with subscriptions.

• 05 Governance, evaluation & measurement

  Only 18% of companies your size are confident they could pass an independent AI governance audit, and 12% have a tested incident playbook, while most already give AI systems access to their data.

Every one of these is delivered the same way: scoped to one outcome, built against your systems, tested on your real cases, then run and improved for you. All five services

We start where the numbers already work.

• 01 Customer support

  $1.84 per self-service contact against $13.50 with a person. Realistic resolution is 40 to 60% in the first months; teams that skip the knowledge-base work stall at 30 to 45%. We do the knowledge-base work.

• 02 Internal knowledge & research

  The second most deployed use case. Structured, cited access to your documents, tickets and systems for the people who spend their day searching for answers.

• 03 Document processing

  Contracts, claims, invoices, onboarding packs. Extraction and routing with a human checkpoint wherever the cost of an error is real.

• 04 Voice

  Inbound first, since it is over half the market. Sold as its own line with its own acceptance tests, not as a feature of support.

• 05 Back office

  Finance, HR and operations workflows that run on approvals, lookups and handoffs. The ones nobody enjoys and everybody depends on.

What we won't lead with: AI outbound sales. Reply rates halve as volume rises, AI-booked meetings convert at 28% against 47% for people, and nearly half of teams hit email deliverability walls within 90 days. If a use case won't hold up, we say so on the first call.

Sources: Lorikeet AI customer service statistics; LangChain State of Agent Engineering; AI SDR statistics 2026