The work runs itself.
You stay in control.
We build agentic AI, automation and security systems that carry real operations end to end — with the permissions, evidence and audit trail that let you give software genuine authority.
Ninety minutes, no pitch deck. Around a third end with us saying you don’t need us.
- Systems in production
- 240+Systems in production
- Straight-through resolution
- 94%Straight-through resolution
- Mean time to contain
- 2.8 minMean time to contain
- Annual client savings
- $4.1MAnnual client savings
Order-to-cash
run 4,912- Ingest supplier invoicesDone1,284 documents
- Match line items to purchase ordersRunning1,241 matched
- Resolve pricing exceptionsRunning12 open · 31 cleared
- Post approved batch to ERPQueuedwaiting on approvals
- Brief the controllerQueuedscheduled 08:00
Every action policy-checked, logged and reversible.
Straight-through
Cost per document
Hours returned
We build on the platforms you already run
Why this is hard
Everyone has been sold AI once.
Almost nobody was sold the hard part.
The model was never the constraint. Permissions, evaluation, cost ceilings, exception handling and an audit trail are — and they are exactly what gets cut when a pilot needs to look impressive by Thursday.
What we do differently
- Measure the baseline before writing code, so improvement is a fact rather than an impression
- Design permissions, spend ceilings and escalation before the first agent exists
- Run in shadow mode until the numbers agree — that is where the real exceptions surface
- Return at 90 days and re-test the business case, publishing the result either way
- 87%never reach production
Not because the model was weak. Because nobody owned the operating change, and security review had no fast lane.
- 11 wksaverage security review
When every project queues behind the same review regardless of risk, teams quietly stop proposing them.
- 0business cases re-tested
The case is written to secure funding and never opened again, so nobody learns which estimates were optimistic.
The pattern is consistent. Across forty-plus enterprise engagements, the projects that reached production had a named business owner, a risk framework with a real fast lane, and a verification date in the calendar before anyone wrote code.
Systems in production
Across finance, healthcare, logistics, retail and public sector
Annual client savings
Verified against pre-engagement baselines
Platform uptime
Trailing twelve months across managed environments
Mean time to contain
From first signal to contained incident, autonomous response
What we do
Ten disciplines. One engineering standard.
Most of our work starts in one service and ends up touching three. That is why they are built by the same people, to the same bar, rather than sold by separate teams.
AI Agents
Give software authority. Keep the receipts.
Deny by default
Agents can only call the tools they were granted, with scoped, revocable credentials per environment.
Thresholds you set
Anything above your confidence or value line routes to a named approver with the full reasoning attached.
Scored every build
A prompt or model change ships only when it beats the version already running, on your own evaluation set.
Agent runtime
Guardrailed- 01
Observe
Reads your systems of record — tickets, ERP, mailboxes, telemetry — as structured context.
- 02
Decide
Plans a route, selects tools, and prices the risk before it commits to anything.
- 03
Act
Executes through real APIs using scoped, revocable, individually-audited credentials.
- 04
Verify
Grades its own output against policy and known-good outcomes. Escalates when unsure.
Learn. Every run is scored, replayed into an evaluation set, and promoted only when it beats the version already in production.
AI Automation
The process that keeps breaking, rebuilt as software.
Redesign before automating
We strip out steps that exist only because of a system constraint nobody remembers. Usually 15–25% of them.
Exceptions are designed, not discovered
Every workflow has a path for what it cannot handle, with an owner, context and an SLA that is monitored.
Reconciled every run
End-of-run checks prove what went in came out. Discrepancies raise themselves instead of waiting for month-end.
Workflow topology
LiveTrigger
Email, webhook, schedule
Understand
Classify, extract, enrich
Decide
Rules + model + policy
Execute
Write to ERP, CRM, ITSM
Confirm
Notify, log, reconcile
Human in the loop — by design
Anything above your confidence or value threshold routes to a named approver with the full reasoning trail attached. Nobody has to guess why the system did what it did.
99.94%
Run success rate
1.8s
Median end-to-end
AI Cybersecurity
Contained in minutes. Not discovered in months.
Tune before automating
Two to three weeks reducing false positives first. Automating a 91% false-positive queue industrialises the wrong answer.
Reversible actions only
Session revocation, token invalidation, host isolation. Anything destructive still requires a named human.
We attack our own work
Purple-team validation before any autonomous authority is granted, including prompt injection against our own agents.
Threat surface
Autonomous response- Impossible travel · finance SSOdetected → resolved in 00:41Contained
- Token replay from unmanaged hostdetected → resolved in 02:07Contained
- Privilege escalation attemptdetected → resolved in 04:15Investigating
- Anomalous S3 egress volumedetected → resolved in 06:52Watching
Mean time to contain: under 3 minutes, day or night.
Engineering
Boring, observable systems beat clever ones.
You own everything
Your repositories, your infrastructure, your licence. No proprietary runtime you have to keep paying for.
Handover is a deliverable
Documentation, runbooks and training are scoped and estimated like any other feature, not sold afterwards.
Budgets block the merge
Performance and accessibility thresholds fail the build rather than the launch. It is the only approach that survives a busy team.
1// Tools are typed. An invented value fails here,2// not in your ledger.3export const postInvoice = defineTool({4 name: 'erp.postInvoice',5 input: invoiceSchema,6 scope: ['ap:write'],7 requiresApprovalAbove: 25_000,8 reversible: true,9 async run(input, ctx) {10 await ctx.policy.assert(input);11 return erp.post(input, ctx.audit);12 },13});96%
Delivered on agreed scope
0.9s
Typical LCP
AA
WCAG 2.2 floor
Industries
The constraint is never the technology. It is your industry.
A model risk framework in banking, a HIPAA boundary in healthcare, an accessibility obligation in the public sector. We have shipped inside all of them, and we design to the constraint from day one.
How we work
Five phases. A stop point at every boundary.
You can end the engagement at any phase and keep everything produced up to that point. We structure it that way on purpose — it keeps us honest about whether the next phase is worth funding.
- 01Phase 01
Understand
We sit with the people doing the work and record what actually happens, including the exceptions nobody documented. Then we measure it.
1–3 weeks
- 02Phase 02
Decide
A costed recommendation with the trade-offs written down — including the option of doing nothing, and the option of buying instead of building.
1 week
- 03Phase 03
Build
Two-week increments, always deployable, demonstrated in a running system. Evaluation and security review happen every increment, not at the end.
4–20 weeks
- 04Phase 04
Prove
Shadow or parallel running until the numbers agree with the baseline. This is where the exceptions that would have become incidents show up instead.
2–12 weeks
Selected work
Measured before. Measured after.
Client names are withheld under confidentiality agreements. The numbers are the ones we reported back to them.
In their words
The compliment we hear most is about being told no.
Attribution is by role and sector. Individual names and client organisations stay confidential.
They spent the first two weeks telling us which half of our plan was a bad idea. That is not what we were used to from a vendor, and it is the reason the rest of it worked.
Time to production: 7 months → 9 weeks
We did not add analysts. We stopped asking the ones we had to prove that thirty-six thousand things were fine every month.
Mean time to contain: 6.3 hours → 2.8 minutes
The number I care about is not the 94%. It is that we stopped having the same argument about unposted liability every single month-end.
Cost per document: $2.40 → $0.07
Two previous apps were built for head office. This one was built for the van. That is the whole difference.
Adoption: 12% → 99.4% daily active
The first month was entirely unglamorous technical work with nothing to show a board. It also produced the biggest single jump we have ever had.
Non-brand organic clicks: +214%
We expected a cost-reduction project. What we actually got was the first time anybody here could rebuild our platform from scratch.
$1.9M annualised saving in 90 days
Technology
We pick boring on purpose.
Novel technology carries a maintenance tax your team pays for years. We use the interesting option only where it clearly beats the boring one — and we write down why.
Models & reasoning
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Qwen
Agent & orchestration
- LangGraph
- Temporal
- Model Context Protocol
- AWS Step Functions
- Airflow
- Custom runtimes
Languages
- TypeScript
- Python
- Go
- Rust
- C#
- Swift
Application frameworks
- Next.js
- React
- Astro
- NestJS
- FastAPI
- Django
Mobile
- Swift / SwiftUI
- Kotlin / Compose
- React Native
- Flutter
- Expo
- Kotlin Multiplatform
Data & storage
- PostgreSQL
- pgvector
- ClickHouse
- Snowflake
- Databricks
- Kafka
Cloud & platform
- AWS
- Azure
- Google Cloud
- Kubernetes
- Terraform
- Pulumi
Security
- Microsoft Sentinel
- CrowdStrike
- Wiz
- HashiCorp Vault
- Okta
- Entra ID
Common questions
What buyers ask before the first call
If your question is not here, send it to a senior engineer rather than a form.
Still deciding?
Send the question to a senior engineer instead of a form. You will get a straight answer, and a no if that is the honest one.
With a ninety-minute working session, not a pitch. We want the process that keeps breaking, the numbers you already have, and the constraint you think is immovable. You leave with a systems map and a shortlist. There is no charge and no obligation, and roughly a third of these end with us recommending you do something other than hire us.
Assessments and audits are fixed price and typically run two to four weeks. Production builds range widely by scope — a single automated workflow is a different order of magnitude from an enterprise platform programme. We quote fixed price wherever scope is properly defined, and we publish the number before you commit rather than discovering it in change requests.
Assessment work usually starts within two to three weeks. Build teams are typically available on a four to six week horizon. We do not overbook — if we cannot staff your project with senior people when you need them, we will say so rather than starting with whoever is free.
Yes. We deliver across North America and Europe, with data residency and regulatory requirements handled per market. Our teams work overlapping hours with your core business day rather than expecting you to adapt to ours.
Mostly mid-market and enterprise — typically 200 employees and up, or smaller organisations with unusually complex regulatory or technical requirements. Below that, our engagement model is often more expensive than the value it can return, and we will tell you that directly.
The senior engineers who scoped it. We do not run a model where partners sell and juniors deliver. The people in your first working session are the people writing your code, and they stay on the engagement through handover.
You have working software, documentation, runbooks, architecture decision records and a trained team. Support retainers are available but never required, and nothing we build depends on a licence from us. If you never speak to us again, everything keeps running.
We work inside your infrastructure and under your data governance wherever possible. Where we process data on your behalf, it is covered by a data processing agreement, restricted to the minimum necessary, and never used to train models. Open-weight models can be served entirely within your environment where residency requires it.
Start the conversation
Bring us the process that keeps breaking.
Ninety minutes with our engineers and strategists. You leave with a systems map, an automation shortlist and an honest read on what AI should and should not touch in your business.
What to expect
- No pitch deck, no obligation
- Senior engineers in the room
- A written plan within five days
Prefer email?
support@cyberxsolutions.us