Customer support
Every ticket classified, routed and drafted with the right policy attached. Agents approve or edit before anything goes out.
Measured in first-response time, resolution rate and CSAT
Your demo works. Now what?
sily is an AI consultancy that gets prototypes into production. We test what you've built against real data, engineer everything the demo skipped, and ship systems with results your finance team can verify.
Most AI never makes it past the demo.
The model is rarely the problem. The joins are: messy data, no way to measure quality, costs nobody modelled, and a tool that sits outside the way people actually work.
95%
of organisations saw no measurable return from their generative AI pilots.
MIT NANDA, The GenAI Divide, 202530%+
of generative AI projects were forecast to be abandoned after proof of concept by the end of 2025.
Gartner, July 202440%+
of agentic AI projects are forecast to be cancelled by the end of 2027, over cost, unclear value or weak risk controls.
Gartner, June 2025What changes between the demo and production
In the demo
In production
Twenty hand-picked examples
Thousands a day, and the odd ones are the ones that matter
It looks right
Accuracy measured on your real cases, every release
Pennies to run
A cost per transaction someone has to sign off at full volume
Sample data on a laptop
Customer data, permissions, PII and an audit trail
A chat window in a new tab
Inside the CRM, ERP or inbox your team already lives in
One model, frozen in time
Models change every few months, and yours has to survive the upgrade
The builder is watching
Monitoring, alerts, and a human fallback when the model isn't sure
How we get you to production
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1
Stress-test the demo
We run your prototype against thousands of real cases, not twenty. In two weeks you know where it breaks, what it will cost at volume, and whether it's worth shipping.
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2
Set the baseline
Before we change anything, we measure today's process: time, cost and error rate. Then we build an evaluation suite so every change is scored against your own cases.
-
3
Engineer for production
Retrieval over your data, guardrails, fallbacks, human review where it matters, cost controls from day one, all wired into the systems your team already uses.
-
4
Ship it and prove it
Staged rollout, monitoring and a team trained to run it, then an impact report against the baseline. If the numbers don't move, you'll hear it from us first.
Where production AI pays for itself
We focus on high-volume, rules-heavy work where every result can be checked. That's where the return shows up in a quarter, not a keynote.
Finance operations
Invoices read, matched to purchase orders and posted. Only the exceptions reach a person, with the mismatch already explained.
Measured in touchless rate and days to close
Sales and bids
RFPs and security questionnaires drafted from your approved past answers, with the gaps flagged for an expert.
Measured in hours per response and responses per quarter
Contracts and compliance
Agreements checked against your playbook. Risky clauses are flagged with the reason and the exact wording cited.
Measured in review time and issues caught before signature
Internal knowledge
Answers from policies, wikis and past tickets, each one linked to its source and limited to what that person is allowed to see.
Measured in questions deflected and time to answer
Engineering
Legacy migrations, test coverage and code review support, with your engineers approving every change that merges.
Measured in throughput and escaped defects
What you walk away with
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Hundreds to thousands of your real cases, scored automatically, so every prompt, model or code change shows whether it made things better or worse.
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Cost per transaction at today's volume and at ten times it, with the levers to bring it down: caching, smaller models, batching and routing.
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Running in your cloud and your accounts, integrated with your tools, with monitoring, alerting and a human fallback when the model is unsure.
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PII handling, prompt-injection defences, access controls and audit logs, documented so your security and legal teams can review them.
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Before-and-after numbers against the baseline we measured on day one, in the format your leadership already reads.
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Runbooks, documentation and hands-on training, so your people can operate and extend the system without us.
How we're built
Baseline first
We measure the current process before we touch it. If we can't show the before, we can't prove the after.
We'll tell you when not to build
If the stress test says the numbers don't add up, we say so. Stopping at week two is cheaper than stopping at month nine.
Your stack, your data
We build inside your cloud and your accounts, and hand over every line of code, every prompt and every evaluation.
Model-agnostic
We pick the model that fits the job and the budget, and design so you can swap it when a better one ships. No reseller deals.
Ways to work with us
2 weeks
Demo stress test
We run your prototype against real data and hand you a go or no-go: where it breaks, what it costs at volume, and the plan to ship it.
6–12 weeks
Production build
One use case taken to production with evaluations, guardrails, monitoring and a measured before-and-after.
Ongoing
Scale
More use cases on the same foundation, with shared evaluations, cost controls and governance, shipped every month.
No prototype yet? We start with a one-week opportunity map and build the first demo with you.
Bring us the demo.
Send a link, a recording or a paragraph about what you've built. We'll reply with what it would take to put it in production.
Email hello@sily.io