We turn conversations
into operations.
Every customer conversation creates work behind the scenes. Most organisations do that work by hand, or with tools that never quite landed. We run it for you, as a managed service.
The work between conversation and operation is costing you a fortune.
Conversations land — by phone, email, chat, ticket. Behind every one of them, something has to happen: a booking, an update, a charge, an escalation. The work of getting from one to the other is invisible on the P&L. At your volume, it shouldn't be.
People doing the work directly
Receptionists, support agents, account managers — keying conversations into systems all day.
People propping up half-built tech
Vendor chatbots, SaaS tools, auto-responders. Bought to reduce cost. Humans absorb the gap.
Internal AI projects that didn't land
Plausible-looking, costly to run, hard to test, harder to trust.
When any of these fails, the work lands back as more conversations. Chase-ups. Complaints. Re-work. The cost compounds.
People doing it directly
Half-built tech
Internal AI projects
The transposition layer — three forms of middleware feeding operations, with a failure-work loop back to channels.
This cost is now a function you can manage.
It's called Conversational Ops.
Until recently, the work of turning conversations into operations didn't have a name. It sat hidden across payroll, vendor tools, and abandoned tech projects. That's changed. The technology now exists to run this work as a proper function — measured, improved month over month, accountable to the P&L. Most organisations don't want to build that function themselves. We run it for them.
Direct cost reduction
The work shrinks month over month. Savings compound.
Failure work eliminated
Handle the first conversation right; the chase-up never happens.
Capacity unlocked
Your best operators stop keying and start advising.
cost reduction within twelve months. The curve keeps going.
+ Build vs buy: what running this function in-house actually costs expand
Annual run-rate, by component. Standing this up in-house means hiring, infrastructure, and the judgment risk that only comes from years of doing it.
| Component | Build in-house | AllSet |
|---|---|---|
| ML engineers | 3–5 hires | Shared, included |
| Data engineers | 2–3 hires | Shared, included |
| Evaluation engineers | 1–2 hires | Shared, included |
| Infrastructure | Build & maintain | Run on our platform |
| Time to first value | 12–24 months | 6–10 weeks |
| Judgment risk | You learn by doing | We've already learned |
| Annual run-rate | £1.5–3m+ | A slice of that |
Find the hotspots. Run the function.
Two stages. The first finds the work worth doing. The second runs it.
Professional Services
Hotspot audit, cost quantification, data salvage, sequenced rollout. Fixed scope.
In detail →Managed Service
Capture, train, deploy, improve. The function, run for you. Monthly retainer.
In detail →Professional Services
Hotspot audit · cost · salvage. Fixed scope.
Managed Service
Capture · train · deploy · improve. Monthly retainer.
They run in parallel once the foundation is set.
The hard parts are already built. And running.
Conversational Ops is a function most organisations have never run. The mistakes you'd make standing it up from scratch are the mistakes we've already made.
AllSet.chat runs every day for thousands of solo professionals.
Eval frameworks, training pipelines, intent capture — built and hardened.
Previous experience at scale with National Grid, Total Energies, Santander, EDF.
AllSet.chat
Thousands of solo professionals — every day in production.
AllSet Enterprise
Phone · email · chat · ticket — run as a managed service.
The hard parts are already built. And running.
+ What this means in practice expand
- · You're not the first.
- · The risk you're avoiding is the one you can't see.
- · The judgment is the product.
The numbers, the names, the credentials.
+ Frequently asked questions expand
Who owns the data? +
You do. Conversations, training datasets, and trained models are yours. We process under contract; we don't train shared models on your data.
What happens if we leave? +
You take the data and the models with you. No lock-in clause.
How do you handle PII? +
Stripped at capture. Redacted before training. Audited as part of the pipeline.
What does deployment actually look like? +
Phased — shadow, assist, auto — gated by evaluation results. Nothing goes to production without your sign-off.
What if the model gets it wrong? +
Escalation to a human is a first-class behaviour. The Lead reviews failures with you each cycle.
How is this different from a chatbot? +
A chatbot replies. This function replies and triggers operations in your downstream systems.
How is this different from hiring an internal AI team? +
Speed, cost, judgment. You skip the build years. You pay a slice of a shared engine.
What does it cost? +
Professional Services is fixed-scope and quoted per engagement. Managed Service is a monthly retainer scaled to your volume. Both are sized after the hotspot audit.
How long is the commitment? +
Twelve-month minimum on the Managed Service.
Who do we talk to day-to-day? +
Your Lead. One person, named on day one.
Run your conversations like the rest of your business.
A short call. We'll tell you whether your organisation is a fit, and where we'd start.