The difference isn’t the model. It’s what the model knows about your business.
Ascelsion anchors AI in your own code, documents and records. Every answer cites its source, every change is approved by a person, and accuracy is measured before anyone relies on it.
What approvals do we need before onboarding a new logistics vendor?
Example. Same model, same question, both ways.
The category manager approves vendors under USD 70,000 a year.
Above that, the CFO signs as well.
Vendors handling customer data need a data-protection review before the contract is issued.
What breaks if we rename the STATUS column on the customer table?
Example. Same model, same question, both ways.
Customer.java maps it, and 14 classes read it through the data-access layer.
Two PL/SQL packages filter on it, for billing and accounts.
The nightly active-customer report groups by it.
Why did operating costs rise in March?
Example. Same model, same question, both ways.
Two branch leases renewed at a higher rent.
A one-off audit fee was booked in March.
Staff costs were flat against February.
Every claim points to the record it came from.
3 of 3 claims citedMeasured before anyone relies on it
We report a range, not a score.
Thirty-six questions asked three times is enough to say where the true rate sits, not to pin it to a point. So every number we publish comes with the range the data supports. The dot is what we saw; the line is where it could be.
When the ranges don’t overlap, the gain is real. When they do, we say so.
The bar is written down before the run.
Before we measure anything, we write the number that would justify going further. For a pilot that is a gain written into the charter before the first question is asked. Then we run, and the bar does not move.
If the range clears the line, we propose the next phase. If it does not, we say so.
People act on the answers
When every claim can be checked in a click, engineers and analysts stop waiting for the one senior who knows.
Measured by the correct and cited rate
Time comes back
Questions answered in minutes, reviews that arrive with the impact already mapped, new joiners productive sooner.
Measured on your own baseline
AI reaches the business
Teams adopt what they can trust, so the work moves past the pilot and into how the business runs.
Measured by weekly use and usefulness ratings
Three things that are always true.
Every answer cited.
Each claim links to the file, document or record it came from. If there is no source, the answer says so.
Every change approved by a person.
Bots draft, review and suggest. Only people on your team can approve and merge.
Every call logged.
Who asked, which model answered, what was redacted and what it cost, in your own security logs.
Three service lines. One team from the first workshop to production.
Take them in sequence or start where the need is. Each ends in something your team runs, never in a dependency on us.
Advise
Consulting
Where AI pays off in your operation, where it doesn’t yet, and how to govern it before the first build.
You leave with an opportunity map, a pilot shortlist and a governance baseline.
Build
AI solutions
Grounded assistants, agents and automation, built inside your codebase and your workflows, with evaluation and a human gate on every change.
You get working systems in production, measured against targets agreed up front.
Sustain
Governance and enablement
What every later use case inherits, and the people who will run it.
You keep the gateway, the evaluation harness, the AI inventory, and a team trained to run them.
One platform, installed inside your network.
Built once in the first engagement, reused by every team that follows, and yours to keep when we leave.
Your network
- What each team seesCoding agents · Codebase chat · Agent assist · Analyst drafts
- Grounded knowledgeCode map, policies, procedures and records. Every answer cited.
- Governance and evaluationAI inventory, expert question sets, a named owner for every use.
- AI gatewayIdentity, redaction, audit and spend caps on every model call.
- Your systems, read onlyCode and its history, documents, ledgers, tickets
The only route outModel provider · Zero retention · In-region where required
The code map, tools, review bots and evaluation harness are built by Ascelsion and licensed to you permanently. The gateway and monitoring are established products we configure for you.
Security and compliance, built in
Every control comes with a test.
Only models and inference endpoints you have approved are connected. Nothing else can be reached.
Tested by
We point a request at a model that isn’t on the approved list. It must be refused, and the refusal must be logged.
What it means for you
Every AI answer in your business comes from a model you approved.
Approval rules are configurable, every approval is logged with a name, and bots can never approve.
Tested by
We try to approve a change with a bot’s own account. It must be refused, and the attempt must appear in the audit log.
What it means for you
Every change that ships has a named person who approved it.
Personal data, secrets and identifiers are detected and redacted before any prompt leaves.
Tested by
A red-team set of 50 secrets and identifiers must all be caught, in our build pipeline and again in your network.
What it means for you
Privacy obligations hold even when someone pastes the wrong thing.
Models, routes, limits and the off switch are configured in one place.
Tested by
Your IT team runs the drill, timed, at deployment and at handover: one switch stops every model call while core systems keep running.
What it means for you
One place to set the rules for every AI use, and to stop all of it at once.
Prove it before you scale.
Four steps, each ending in something you keep. Stop after any of them.
Discovery call
Free, thirty minutes
Paid diagnostic
Fixed fee, two weeks
Pilot
Milestones shown live
Scale
A separate decision
Every diamond is a go-or-stop gate. Stop at any of them and keep everything above it.
A paid diagnostic, not a free pitch.
A small fixed fee keeps the advice honest, and the plan is yours either way.
An explicit no.
If AI isn’t worth it for your case, we tell you.
Stop conditions in writing.
Agreed before we start, so a phase that isn’t earning its keep ends early and cleanly.
You own the result.
Code, prompts, evaluations and runbooks. No runtime you can’t leave.
Why context decides the return.
Independent research, not vendor claims.
95%
of organisations saw no measurable return from generative AI. The barrier was systems that don’t adapt to context, not the quality of the model.
MIT NANDA, The GenAI Divide, 2025
0–10%
net productivity gain on complex legacy work, against 15–20% overall. Legacy estates are where grounding earns its keep.
Stanford software engineering productivity research, 2025
Amplifier
is DORA’s word for AI: it magnifies the system it lands in. Internal data that AI can reach is among what makes it pay.
Google Cloud DORA, State of AI-assisted Software Development, 2025
Insights
The evidence, read for people who have to act on it.
The company
Three commitments, on every engagement.
Integrity
Your data stays where it must. Every AI change is owned by a person. We put in writing what would make us stop, and we tell you when AI isn’t the answer.
Intellect
Senior engineers in the room and in your codebase, not a delivery pyramid.
Impact
Targets agreed before we start, evaluation that proves them, foundations your next project reuses, and a team trained to run it without us.
Leadership experience at Atlassian, Adobe, McKinsey Digital Labs, Cisco and Yahoo.
Meet the teamQuestions careful teams ask first.
Anything else, bring it to the discovery call.
Do we own what you build?
Yes. The code, models, prompts and documentation are yours. No runtime you can’t leave, no lock-in. Ownership is the point, not an add-on.
What if AI isn’t the right tool for us?
Then the diagnostic says so, for a small fixed fee, and we tell you plainly. We’d rather lose the build than ship something that doesn’t earn its keep.
We’re in a regulated industry. Can you work inside our constraints?
Yes. We design to the regulator’s expectations from day one: an inventory of every AI use, a named oversight model per use case, data kept in-country where rules require it, and the ability to switch any AI off. If a rule means a use case can’t run, we say so before building it.
How do you handle our data?
It stays in your environment wherever possible, with documented processing, built with GCC and India data rules in mind. The detail is in our data-handling note.
Who maintains it after you leave?
Your team, trained by us, with a written runbook. We can stay on call if you want, but the system is built to run without us.
Bring one question your teams can’t answer reliably today.
In thirty minutes we’ll show you how it would be anchored in your data, and how we’d measure the answer.
No pitch.
A working conversation about your operation.
An honest answer.
If AI isn’t the right tool, we’ll say so.
A clear next step.
If there’s something worth building, you’ll know what the diagnostic covers.