AI deployment for mid-market businesses
Most mid-market companies have already tried AI. Very few have anything running in production that finance can see. We scope one workflow, build it against your real systems, prove it with acceptance tests, and run it after launch.
Working pilot in 30 days
Out of the pilot, into production, onto the P&L
A 30-minute scoping call. No deck.
- Scope & data readiness
- Build against your systems
- Evaluation & acceptance
- Deploy with controls
- Run & improve
Why most AI stalls
95%
of generative AI pilots produced no measurable business return
MIT Project NANDA, 2025
5%
reached production, out of the 60% evaluated and 20% piloted
MIT Project NANDA, 2025
63%
of executives report no measurable earnings impact from AI
McKinsey State of AI, August 2026
40%+
of agentic AI projects will be cancelled by the end of 2027
Gartner, 2025
The usage is there. The earnings are not.
Eight in ten people say AI makes them personally more productive. Just 6% of companies report an earnings impact of 5% or more. The gap between the two is implementation: connecting the model to your real systems, testing it against your real cases, and keeping it working after launch. That is the work we do.
Why mid-market
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
How we work
Five phases. Evaluation is one of them.
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01
Scope & data readiness
One workflow, one owner, one measurable outcome. We check whether your data can support it before anything is built; at least half of advanced AI projects need data work first.
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02
Build against your systems
Results come from integration depth, not model choice. One support deployment went from 32% to 77% resolution by adding 25 API integrations. We build and own the connector layer.
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03
Evaluation & acceptance
A golden set of 100 to 300 expert-validated cases split across common, tricky and adversarial. Published failure budgets. Statistical acceptance instead of a demo and a signature.
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04
Deploy with controls
Tiered autonomy with people in the loop: read-only, reversible, customer-facing, high-risk. Circuit breakers on spend, iterations and consecutive failures. A kill switch from day one.
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05
Run & improve
Systems that don't retain feedback repeat the same mistakes; MIT named that as the root cause of the 95%. We operate what we build on a monthly retainer, measure it, and improve it.
Sources: Accenture; Lorikeet resolution benchmarks 2026; MIT Project NANDA
Where it pays back first
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.
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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.
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03
Document processing
Contracts, claims, invoices, onboarding packs. Extraction and routing with a human checkpoint wherever the cost of an error is real.
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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.
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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
Pricing
Quoted to scope. Never by the seat.
What an engagement costs depends on the workflow, the systems it has to touch and how much of the operation you want us to run afterwards. Tell us the workflow and we come back with a written quote covering build, run and, where the work suits it, verified-outcome terms.
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01
Build
A fixed price for a fixed scope, agreed before work starts.
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02
Run
A monthly retainer for operation, evaluation, governance and improvement.
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03
Verified outcomes
Where it fits, a per-outcome price that applies only to results verified against criteria we agree up front. No charge when a case escalates to a person.
Talk to us
Tell us the workflow that slows your business down.
In 30 minutes we'll tell you whether it is a fit for AI, what it would take to put into production, and how we would prove it works before you pay for outcomes.