Vectoresque is a senior AI and automation practice serving enterprises and public-sector
organisations across the Nordics and Baltics. We build production AI agents and
then run them against agreed service levels, we automate business-critical
processes, and we help executives work out which use cases deserve funding.
Our agents ship behind a four-rung authority ladder whose top rung is never act,
enforced by a build-time guard rather than by asking the model nicely.
Read the file
Twenty-five years in enterprise IT. Eleven building on AWS, six of them inside AWS,
leading cloud and AI work for regulated and public-sector organisations across eight
countries. Fifteen in process orchestration and automation, which is what the agentic
work is built on.
The gap we close
Most organisations are not short of AI ideas. They are short of people who can hold the
architecture, the business case and the code in the same head.
The pilot demo is easy. What is hard is the part after it: the agent that behaves under real
data, the process that still reconciles at month-end, the audit trail a regulator will accept,
the cost curve that does not detonate at scale, and the honest answer to which of the
fourteen proposed use cases deserves funding.
That is the work we take. Twenty-five years of business-critical enterprise systems sits
behind it, and so does daily hands-on work on multi-agent AI. Both halves are required:
advice without delivery produces slideware.
What we do
We build agents, then we run them. The rest supports those two.
Managed agents in production
Running an agent well is a different discipline from building one. We host, instrument
and operate agents against agreed service levels, assembled from established
observability and evaluation tooling that your own team could take over. Traces on open standards, evaluation suites that run on every release and on
sampled live traffic, cost and latency budgets per route, versioned prompts and models
with a rollback path, and review queues wherever the trust ladder requires a human.
The measurement exists to drive the next change. Each month you get what the metrics
say, what we changed because of them, and what it cost. You get an agent that improves;
we carry the operational load. We are happy to take over agents we did not build.
Hosting & deployment
OpenTelemetry-based tracing
Eval suites & regression gates
Live-traffic sampling & scoring
Cost & latency budgets
Prompt & model versioning
Drift and incident alerting
Human review queues
Monthly improvement cycle
AI agents in production
Agentic systems that carry out real work. We design the orchestration, the tool
boundaries, the guardrails and the human-in-the-loop stop conditions, then build and
instrument them so you can prove what an agent did and what it cost.
Multi-agent orchestration
Tool & MCP integration
Evaluation & accuracy measurement
Guardrails & delegated autonomy
Cost-aware model routing
Agentic developer tooling
Automation & process orchestration
The unglamorous engine room, and our oldest discipline. We automate business-critical
processes end to end across finance, ERP, data, provisioning and invoicing. This is the
practice we grew from a single customer to ten-plus enterprises running millions of
automated processes a year.
Process discovery & candidate scoring
Workload automation
System-to-system integration
Exception handling & recovery
AI only where it beats rules
AI in the enterprise
Target-state architecture for AI and cloud in environments with real constraints:
regulated industries, public sector, critical infrastructure. Security and resilience,
data boundaries, compliance evidence, European sovereignty and procurement that survives
a challenge all belong in the first architecture diagram.
AI & cloud target-state architecture
Landing zones & platform design
Data boundaries & sovereignty
Compliance & audit evidence
Build-vs-buy and vendor strategy
AWS · multi-cloud · hybrid
Executive counsel on AI & IT
A senior, vendor-independent counterpart for boards and management teams. We run
C-level sessions and working workshops that end with a decision: what to fund, what to
kill, what to sequence, what it costs, and what capability you need in-house versus
bought.
Every step produces something you can act on. You can stop after any of them.
1
Working session
Half a day to a day · fixed fee from €1,500
We go through the situation, the candidate use cases, the systems and the constraints.
You leave with a written, costed direction, ranked and argued, useful whether or not
you continue with us.
2
Proof that decides
2–6 weeks · fixed scope
One narrow build against your real data and real systems, designed so it either confirms
the premise or kills it cheaply. We define the success criteria before we start and
report against them honestly, including when the answer is no.
3
Build & hand over
Ongoing · sprint or retainer
Production build with your team in the room: tests, deployment, runbooks, evaluation
harness and documentation. The objective is a system your own people can operate and
extend without us.
4
Standing counsel
Monthly · fractional
A senior architect and advisor on call for design reviews, vendor decisions, escalations
and board material. The capability of a lead architect without the headcount.
Principles
Senior-only. The people who scope the work build it, so nothing is lost between whoever understood the problem and whoever writes the code.
Calibrated honesty. If AI is the wrong tool for your process, we will say so in the first session. A rules engine that works beats an agent that impresses.
No lock-in by design. Your architecture, your repositories, your accounts. We stay portable across models, clouds and runtimes on purpose.
Under the hood
Anyone can claim rigour. Here is some of ours, working.
Two things below are live: an interactive model of how we drive confabulation out of AI
output, and real artifacts from the harness we build with. Move the sliders, read the
schemas.
Interactive · error compounding
Why one clever prompt is not a quality strategy
A single verification step leaves you at the mercy of its own miss rate. Independent
layers multiply: each one only has to catch some of what the last one let through.
This is the Swiss-cheese model, and it is what stands between an AI feature that demos
well and one you can put in front of an auditor. The arithmetic below is real; the
starting percentages are only illustrative. Each slider sets a layer's miss rate, the
share of false claims it lets through. Set them to your own numbers and watch what
reaches the customer.
1.69%residual error reaching the customer
Raw model output1000
After L0450
After L2113
After L334
After L417
Of 1,000 raw candidate findings, 17 unverified claims survive
to the report. The best single layer alone would let 250 through.
Layers this weak are not independent enough to rely on. That is a design smell.
Independence is the assumption doing the work here, and in practice layers correlate. The
honest number therefore comes from a measured ground-truth benchmark, which is why we
build one. We would rather have that argument with you before a system ships.
If this is the conversation you have been trying to have with a supplier, we should talk.
What "production-grade agents" looks like in the file
Agents become trustworthy when their output is typed, their authority is bounded, and
their reasoning survives an audit. Four contracts from the harness we run every day,
abridged from the real source. Start with the trust ladder: it is the governance
conversation your risk function wants to have, answered in code.
// Every finding an agent emits is typed, evidenced and refutable.// No free-form prose reaches a customer report.
{
"id": "fnd_01J8…",
"class": "functional-blocker | comprehension | trust | accessibility",
"severity": "critical | major | minor",
"claim": "One sentence. Falsifiable. No hedging.",
// L0 — the deterministic anchor. A fact, not an opinion."outcome_grounded": {
"expected_state": "invoice.status == 'sent'",
"observed_state": "invoice.status == 'draft'",
"source": "system-of-record"
},
// L2 — the skeptic must fail to refute it, not merely agree."adversarial": {
"verdict": "survived",
"refutation": null,
"tier": ">= generating tier"
},
// L3/L4 — nondeterminism converted into a statistic."reproduction": { "k": 5, "recurred": 4, "frequency": 0.8 },
"corroborated_by": ["run_b21", "run_b44"],
"confidence": 0.86,
// Auditability: anyone can re-derive this conclusion later."evidence": ["screen_014.png", "dom_014.txt", "trace_014.jsonl"],
"manifest": "run_2026-08-11T09:14Z / commit 4f2a9c1"
}
# Delegated autonomy — what an agent may decide alone,# and where it must stop and ask a human. Bounded authority# is what makes autonomy acceptable to a risk function.## Decide alone
- implementation detail inside an approved spec
- naming, structure, test cases, refactors behind a green suite
- retry while each attempt adds a new evidence-backed hypothesis## Stop and ask — hard gates
- money, auth, tenancy, personal data, destructive migration
- anything that changes a contract another system depends on
- scope growth beyond the approved brief
- repeated identical failure with no new hypothesis # the loop-breaker## Verification rule
- reviewer tier >= builder tier on every critical path
- a cheaper model never approves a stronger model's change
- one clean post-fix round is required before close
## Durable state
- the git-tracked ledger is the source of truth, not the transcript
- every wave closes with: decision, evidence, exact SHA, next action
- if the tooling disappeared tomorrow, the ledger is still readable
in a text editor # no lock-in, by construction
// Shipping AI to a non-technical user who is personally liable for// what the software does. Four rungs decide how far the AI may go —// and the top of the ladder is "never act", not "act carefully".export const AI_TRUST_RUNG_VALUES = [
"find", // surface it"check", // verify completeness, readiness, risk"draft", // write it — a human signs it"never_act", // money, access, roles, legal, privacy, external effect
] as const;
// Anything touching money, access, roles, official records, privacy or// the outside world resolves to never_act — a classification the// surrounding code enforces.export functioninferAiTrustRung(input): AiTrustRung {
if (/money|access|role|official|privacy|external|legal/.test(v)) return"never_act";
if (/draft|minutes|reply|message|certificate/.test(v)) return"draft";
if (/check|review|readiness|completeness|risk|gap/.test(v)) return"check";
return"find";
}
// The part that makes it real: a build-time guard over the product's// OWN user-facing copy. The interface cannot claim the AI decided,// sent, approved or collected anything — the suite fails first.export functionassertNoAiAuthorityOverclaim(text: string): void {
for (const pattern of AUTHORITY_OVERCLAIM_PATTERNS) {
if (pattern.test(text)) {
throw newError(
"AI copy must not imply legal, money, access, official, " +
"privacy, or external-effect authority."
);
}
}
}
# Cost-aware routing. Capability where it changes the outcome,# cheap where it does not, deterministic code where a model# adds risk without adding judgement.deterministic:
use: [ledger-moves, filtering, joins, dedup, validation]
why: "faster and auditable; a model here is pure downside"volume_tier:
use: [bounded-inventories, fixtures, format-conversion]
never: approving critical-path logicworkhorse_tier:
use: [implementation, research, debugging, standard-review]
effort: medium# escalate only where evals show real gainfrontier_tier:
use: [architecture, schema-design, ambiguous-decisions,
security-privacy-money-review, cross-agent-synthesis]
effort: highinvariants:
- "verification tier >= generation tier on critical paths"
- "agent count is not evidence of progress"
- "reuse green proof only for the same SHA, scope and config"
- "benchmark every route change; one variable at a time"
Systems we have built
Two platforms of our own, specified and shipped through that harness.
Client work is under NDA, so the systems we can open up are ours. Both are our own
pre-launch products: the first deploys to production on every green
pull request and is in controlled pilot, the second is feature-complete and not yet
released. We would rather show you real internals than a logo wall.
Regulated SaaS · Finland · controlled pilot
A management platform for Finnish housing companies
Software for volunteer boards who are personally liable for getting the law
right. The users are non-technical and a wrong answer has legal consequences, which
drives every decision in the system.
The law is the specification
The Finnish Housing Companies Act is encoded directly: general-meeting quorum computed at share-group grain so co-owned apartments cannot double-count, per-item majority evaluation including qualified majorities, and statutory notice gates that quote the section verbatim rather than summarising it.
Money that has to reconcile
ISO 20022 bank-statement ingestion (CAMT.053/054), Finnish payment references with the 7-3-1 check digit and ISO 11649 RF, and a pure allocator that applies debt-collection law's protective ordering — oldest period first, operating before capital before usage, deterministic tie-break. Over- and underpayment become a carried balance, never an error. Integer cents end to end.
AI on a leash, by construction
The assistant ships behind a four-rung trust ladder: find, check, draft, never act. Anything touching money, access, roles, official records or privacy resolves to the top rung automatically. A build-time guard over the product's own copy makes it impossible to ship an interface claiming the AI decided, sent, approved or collected anything. Retrieval runs behind a default-off flag with a PII-minimising sanitiser, keyword fallback, PII-free invocation logs and an evaluation gate.
Release discipline
Trunk-based with feature flags: ship dark, enable per cohort, disable by flag instead of rolling back. Across the waves that recorded an explicit tally, adversarial review logged 160 findings before merge, 14 of them high severity.
1,174feature specs in a git-tracked ledger
5,817automated tests, up from 1,080
139database tables
111screens across board, resident and firm views
Consumer marketplace · Finland · pre-launch
A two-sided marketplace with pickup logistics
A marketplace is where concurrency, privacy and money stop being theoretical. Two people
reserve the last item in the same second; a seller's home address must never become
public; a retried background job must not charge or notify anyone twice.
Correct under concurrency
Reservations are derived, never stored as a mutable counter, and taken under row-level locks with a fixed acquisition order so concurrent claims serialise instead of deadlocking. Background sweeps are idempotent and watermarked, so a retry is a no-op rather than a duplicate charge.
Privacy that cannot leak
Locations are grid-snapped before they are written. Exact coordinates are never stored anywhere in the system, so no query, export or breach can reveal them. Designing the leak out beats guarding it.
Growth engineered in
Structured data and social images, generated city-by-category landing pages, saved-search alerts on a daily cron, and HMAC-signed one-click unsubscribe links. The acquisition and retention surface is treated as product, with tests.
Adversarially audited
A layer-by-layer adversarial review produced 55 confirmed findings (6 high, 10 medium, 39 low) and refuted one. The refutation is the point: a review process that never rejects its own findings is not a review process.
1,517unit and end-to-end tests
97API endpoints
21end-to-end journeys under Playwright
0exact coordinates in the database, by design
What every change passes before it ships
Deterministic gateTypecheck, lint, full test suite, production build and dependency audit, all automated.
Behaviour gateThe new critical path is driven end to end. Rendering without crashing is not evidence that it works.
Adversarial reviewSecurity, money and permission lenses run until a round finds nothing new.
Design-quality gateThe surface is reviewed through the real user persona against a six-dimension rubric. Serious failures block the ship.
Ledger closeDecision, evidence, exact commit and next action recorded, so the reasoning survives the people who had it.
Before Vectoresque
Vectoresque Oy was founded in 2026. The experience it runs on was built
earlier, inside hyperscaler, consultancy and systems-integrator organisations.
~€2M
Funding secured for a new multicloud-orchestration product, owned from vision and business case through to production, across AWS, Azure, GCP and private cloud.
10+
Enterprises running millions of automated business processes a year on an automation practice grown from a single customer, integrating ETL, BI, finance, provisioning and invoicing systems.
8
Countries covered by an AWS public-sector solutions-architecture function built and led from the ground up, with team members in four of them.
122
Delivery waves shipped through our agentic harness on a single platform, each one closed with review evidence, a recorded decision and an exact commit.
Cloud & AI at hyperscale
Six years inside AWS, in lead solutions-architecture roles for regulated industries and in leadership of the public-sector solutions-architecture function for the Nordics and Baltics. Cloud and AI adoption strategy for ministries, agencies and large regulated enterprises, covering European sovereign cloud, compliance frameworks and AI in government.
Automation as a discipline
Fifteen years in process orchestration, workload automation, DevOps, configuration management and infrastructure-as-code, which is the foundation the agentic work is built on. Lead-architect responsibility for enterprise orchestration and hybrid IT across EMEIA, and technical authority for an automation practice sold as a service.
Our product
In development · early access opening
Persona Red-Team
AI personas that adversarially pressure-test whether real humans can use your product,
driving your live application in the messy states your users actually hit.
Synthetic user testing is easy to fake and hard to trust. Persona Red-Team is built around
the part everyone else skips: measured accuracy. Findings are grounded in
real outcomes in your system, anchored by deterministic accessibility and heuristic checks,
attacked by a skeptic agent that defaults to refuting them, and reproduced across repeated
runs so nondeterminism becomes a statistic instead of a guess.
The output is a ranked set of recommendations weighted across your real audience mix. Each
one carries who it helps, who it costs, why it is net-positive on balance, and an audit
trail. All of them are actionable by a human developer or by an AI coding agent.
Outcome-grounded findings
Adversarial verification
K-run reproduction
WCAG & heuristic anchors
Ranked, argued recommendations
Full run auditability
We publish precision and recall against a ground-truth benchmark. Early-access partners
get the benchmark method as well as the tool.
You have AI ambition and a portfolio of proposals, and need someone senior and vendor-independent to rank them and cost them.
A pilot works in the demo and dies on real data, real volumes or real compliance requirements.
You want agents doing genuine work in a business process, with controls a risk function will accept.
You have agents live already and no reliable answer to how well they are performing, what they cost, or whether the last change made them better.
A business-critical process still runs on manual effort, spreadsheets or brittle scripts.
You operate under regulation, sovereignty requirements or public procurement and need architecture that holds up to scrutiny.
You need lead-architect capability now and cannot hire it in six months.
We're the wrong choice if
You need headcount: a large, body-shopped delivery team.
You want a supplier who will agree with a decision that has already been made.
You want an AI narrative for a board deck without anything running behind it.
The requirement is a fixed-price build against a frozen specification nobody has tested against reality.
Questions
What does Vectoresque actually do?
We design, build and de-risk AI and automation systems for organisations where failure is expensive: production AI agents, process orchestration, enterprise AI and cloud architecture, and executive counsel on AI and IT direction. We build hands-on, and the advice is a by-product of doing the work.
How is this different from a large consultancy?
Senior practitioners only. The people who scope the work are the people who build it, so nothing is lost between whoever understood the problem and whoever writes the code. Engagements are deliberately small and evidence-led, and every step is designed so you can stop after it.
Do you work with regulated and public-sector organisations?
Yes. That is our home ground. Our background covers regulated industries and the public sector across the Nordics and Baltics: compliance frameworks, security and resilience requirements, audit evidence, procurement realities and European digital sovereignty.
How does an engagement start, and what does it cost?
Almost always with a paid working session: half a day to a day, fixed fee from €1,500, producing a written and costed direction you can act on with or without us. Larger work is scoped from there as fixed-scope proofs, sprints or a monthly retainer, and managed-agent operation is priced per month against an agreed service level. The first introductory call is free.
Where are you based, and in which languages do you work?
Vectoresque Oy is a Finnish company based in the Helsinki area. We work in Finnish and English, on site in Finland and remotely across the Nordics, Baltics and the rest of Europe.
Can you work with our existing cloud, models and vendors?
Yes. We are model- and vendor-pragmatic by design. Our depth is in AWS and in multi-cloud orchestration across AWS, Azure, GCP and private platforms, and we select model providers to fit your data, latency, cost and sovereignty constraints.
Can you run our agents for us once they are live?
Yes, and it is one of the things we most want to be doing. We host and operate agents against agreed service levels: tracing on open standards, evaluation suites on every release and on sampled live traffic, cost and latency budgets, versioned prompts and models with a rollback path, and alerting on drift and incidents. Every month you get what the metrics show, what we changed because of them, what it cost and what we propose next. We are happy to operate agents we did not build, provided we can first review them against our own bar.
How do you handle our data and confidentiality?
Under NDA as standard, with data boundaries agreed before any system access. Where the work touches personal or regulated data we design the processing, retention and residency arrangements explicitly, and document them so they survive an audit.
Contact
Tell us what you are working on. You will get a considered reply from a senior
practitioner, usually within one business day, including a direct answer on whether we
are the right people for it.