AI engineering studio · Toronto
AI engineering. Built for production.
We build AI systems, agents and applied machine learning for real business workflows — with evaluation, integration and operational requirements designed in from the first commit.
Trusted by teams at
- Northwind Logistics
- Halcyon Health
- Meridian Capital
- Corvid Energy
- Lumen Retail Group
- Atlas Freight
- Verity Insurance
- Bramble & Co.
Evidence · Engineering demonstration
Turning a promising prototype into a system a business can rely on.
A document-heavy back-office workflow: hundreds of contracts and invoices a week, each needing extraction, classification and a decision. A demo model got 80% of cases right in a notebook — and nobody could say which 20% it got wrong, or why.
Constraints
- Sensitive documents that cannot leave the customer's cloud boundary
- Sub-second per-page latency at predictable cost
- A human reviewer stays accountable for every decision
What we built
- A labelled evaluation set built with the domain experts, versioned alongside the code
- A retrieval and extraction pipeline with confidence routing: automatic on high confidence, queued for review otherwise
- Tracing, cost and drift dashboards wired into the team's existing on-call tooling
Outcome
Measured accuracy on the held-out set, a review queue that shrinks as the model earns trust, and an operating manual the customer's own engineers run.
- 2.4M documents decisioned in production
- 98.7% accuracy on held-out evaluation sets
- <640ms p95 latency per page
- 6 systems in production, customer-operated
What we build
Three capabilities, one standard: it has to work on a Tuesday afternoon.
Production AI systems
LLM-powered features and services that survive real traffic: retrieval, structured extraction, summarisation and generation with evaluation harnesses, guardrails and observability.
- Architecture and data-boundary design
- Evaluation sets and regression suites
- Deployed, observable services
Agents & workflow automation
Multi-step agents that plan, call tools and hand off to people at the right moments. Scoped tightly, tested against real transcripts, and instrumented so you can see every decision.
- Tool and permission design
- Human-in-the-loop checkpoints
- Replayable traces
Applied machine learning
Classical and deep-learning models where a foundation model is the wrong tool: forecasting, ranking, anomaly detection and optimisation — trained on your data, deployed in your stack.
- Problem framing and baselines
- Training and evaluation pipelines
- Monitoring and retraining plans
How we work
A short loop, repeated until the system earns its place.
- 01
Frame
We define the decision the system supports, the constraints it lives within and what 'good' measurably means.
OutputWritten problem statement, success metrics and a first evaluation set.
- 02
Build & evaluate
Thin end-to-end slice first, then depth. Every change runs against the evaluation set before anyone sees a demo.
OutputWorking system in your environment with scorecards, not screenshots.
- 03
Integrate
Wire into the real workflow: identity, data boundaries, review queues, existing tooling. This is where most AI projects quietly die; we plan for it from day one.
OutputProduction deployment with runbooks and rollback.
- 04
Operate & improve
Watch cost, latency, drift and human overrides. Feed what we learn back into the evaluation set and the model.
OutputMonthly reviews and a team that can run it without us.
Production requirements
What 'production' means to us.
These are engineering practices we bring to every engagement — not certifications or guarantees. They are how we make an AI system boring enough to depend on.
- 01
Evaluation
Versioned test sets, offline scoring and online sampling. Numbers, not vibes.
What it means to us - 02
Human oversight
Explicit confidence thresholds and review queues. People stay accountable for decisions.
What it means to us - 03
Data boundaries
Clear rules on what leaves your environment, what is logged and for how long.
What it means to us - 04
Observability
Traces for every model call and tool use, wired into the tooling your team already watches.
What it means to us - 05
Latency & cost
Budgets set up front; routing, caching and smaller models used wherever they meet the bar.
What it means to us - 06
Operational ownership
Runbooks, on-call notes and hand-over. The goal is a system your own engineers run.
What it means to us
Why psiberia
A small, senior team with a long-term thesis.
The value in applied AI is shifting from access to models toward the engineering around them: evaluation, integration, oversight and operations. We are building the company that is good at exactly that part — and the reusable methods, tooling and domain knowledge that compound across every project.
- 01
Founder-led engineering
The people who scope the work write the code and carry the pager during launch.
- 02
Methods that compound
Evaluation harnesses, agent tooling and deployment patterns we refine on every engagement.
- 03
Honest about limits
We will tell you when a rules engine, a spreadsheet or no AI at all is the right answer.
Contact
Tell us what needs to work.
Share the workflow, the constraints you are operating under and the outcome you need. We will reply with an honest view of what AI can and cannot do for it.