AI that survives contact with production.
Most AI work stalls between the demo that impressed everyone and the system anyone trusts on a Monday. That gap is evaluations, guardrails, fallbacks and audit trails — the unglamorous half. It's the half we do.
- 01
AI readiness audits
Two weeks, ending in a costed plan you own whether or not you hire us. We map where the hours actually go, what your data will and won't support, and which workflow has the clearest baseline to start from.
- 02
Agentic workflows
Agents wired into the tools you already run, with the permissions scoped tightly and a human checkpoint anywhere a wrong action would be expensive to undo. Not a chatbot bolted onto a sidebar.
- 03
Document and back-office automation
Extraction, classification and validation with confidence thresholds that route uncertainty to a person instead of guessing. The win is in the long tail of near-identical work, not in removing your team.
- 04
Evaluation harnesses
Replay against historical cases with known outcomes, so "it seems better" becomes a number. This is usually where most of the engineering goes, and it's what lets you change the system later without fear.
Audit the code your assistants wrote.
Assistants now write a large share of what ships, and they fail in a specific place. Not syntax — permission boundaries, credential scope, and packages they invented. Each line reviews clean, which is exactly why the scanners are quiet.
- 01
AI-written code review
The authorisation model read end to end: every route, job and handler, and where the check that should guard it actually lives. The fastest-growing defect class of 2026 is the one no pattern scanner reports.
- 02
Prompt injection assessment
Adversarial input against your real system rather than a benchmark — retrieved documents, uploaded files, ticket text, anything that reaches the context window. The finding is what the model then did, not whether it complied.
- 03
Agent permissions and tool sandboxing
An exhaustive tool inventory per agent, the worst case each tool allows, then cutting until a successful injection is survivable. Permission boundaries hold. Prompt-level defences are probabilistic.
- 04
Dependency and supply-chain provenance
Every import reconciled against the lockfile and the registry record, because models invent package names consistently enough for somebody else to register them first.
Built into every system we ship since 2023, offered on its own since 2026. If the code is not ours, say so in the first sentence — the scoping is different.
Read how the audit works →- The human stays on the record
- In anything regulated, a decision needs a person accountable for it and a trail showing how it was reached. We build the review gate in week one, not as a retrofit — teams that add it last find their data model assumed nobody would ever intervene.
- Measure before you automate
- If you don't know how the manual process performs today, you cannot tell whether the system helped. The baseline feels like a delay. It is the cheapest two weeks of the project.
- Deterministic where it matters
- Models are good at judgement and bad at arithmetic. We keep the numbers in code and the language in the model, which is why our reports add up.
- Handover, not dependency
- Your repository, your keys, your runbook, your team able to operate it. We would rather you come back because you want to than because you have to.
How long does an AI readiness audit take?
Two weeks. It ends in a costed plan that names which workflow to automate first, what your data will and won't support, and what the first 90 days should cost. The plan is yours whether or not you hire Everfinity to build it.
Will AI automation replace my team?
No, and projects scoped that way usually fail. The reliable win is in the long tail of near-identical work — the fortieth identical support ticket, the document field retyped for the fortieth time. People stay in the loop wherever a wrong answer is expensive. In practice teams handle more volume with the same headcount rather than shrinking.
We're in a regulated industry. Can we use AI at all?
Yes, but the constraints have to be designed in from week one rather than retrofitted. A decision needs a person accountable for it and an audit trail showing how it was reached. Teams that add the human review gate last usually discover their data model assumed nobody would ever intervene. Everfinity builds the review gate first.
Our data is a mess. Is it too early for AI?
Probably not too early, but data access is likely the first project rather than the model. If the relevant information lives in spreadsheets or nobody is sure where it lives, expect the first weeks to go on making it queryable. That work is unglamorous and unavoidable, and it is cheaper to find out in week one than in month three.
How do you stop the model getting things wrong?
With an evaluation harness, not with confidence. Historical cases with known outcomes are replayed against the system so accuracy is a measured number rather than an impression, and low-confidence results route to a person instead of being guessed. Arithmetic stays in code — models are good at judgement and bad at maths.
Can you audit code our own team generated with AI?
Yes, and it is a different engagement from a penetration test. A pen test attacks the running system and finds what is reachable today; this reads the source and the permission model and finds what is reachable the moment a route is exposed or an agent gets a new tool. Four passes: authorisation boundaries, credential scope, dependency provenance, and what each agent can actually reach. For one service with a defined perimeter, about a week to a ranked list of findings and a second week to land the fixes.
Who owns what you build?
You do. Repository, infrastructure, credentials and runbook are handed over at the end of the engagement, with no proprietary framework you would have to keep paying to maintain.
What does an AI project actually cost?
A two-week discovery is the usual starting point when the data situation is unclear. A four-week proof engagement on a single workflow, priced fixed, is the usual next step. Everfinity quotes a fixed price against a defined outcome — if it takes longer than estimated, that is the studio's problem rather than a change request.
Start with the audit.
Six questions, four minutes, a real plan. No sales call required.