AI-native data & analytics consulting

We don’t bill hours. We hand you finished data work.

PTSB is two principals and an AI execution layer. The AI does the intelligence-heavy build — pipelines, migrations, analytics, reporting. We supply the judgement: what to build, how to architect it, and what comes next. You get the delivered outcome, not a stack of advisory hours.

Start a project See what we deliver

Fixed-scope outcomes, not retainers
AI executes · humans decide
Two senior principals, no handoffs

The shift

Sell the work, not the tool.

Most firms now sell access to AI: seats, hours, “we use the latest models.” That puts the risk on you. You pay for effort and hope it adds up to something shipped. We think that’s backwards. The model is plumbing — interesting, but not the point. What you actually want is the result: the migration done, the pipeline running, the numbers you can trust on Monday.

The old deal

You buy hours and advice
Effort is the product; outcomes are your problem
“We leverage AI” is the pitch
Scope creep, status decks, a team you have to manage

How we work

You buy a defined outcome, delivered
AI does the heavy execution at speed
Two principals own the judgement and the result
You see working data work, not a slide about it

What we do

Three things we deliver, not bill for.

Three productized outcomes, each with a defined finish line. They build on each other — clean data, then trustworthy agents, then the ongoing evaluation that keeps it all working. Priced per engagement, agreed up front — no hourly meter.

Foundation

Operational data, ready for AI

Outcome delivered: Your scattered operational data becomes a clean, queryable context layer your tools and agents can actually use.
What’s included: Source consolidation, modeling, and a governed context/semantic layer — the unglamorous foundation everything else needs. Sold standalone for teams who aren’t ready for agents yet but know their data isn’t.
Who it’s for: Companies whose AI ambitions are blocked by messy, siloed data.

Full build

Production-ready agents on your own data

Outcome delivered: Your AI agents run reliably against your real operational data — grounded, governed, and safe to ship.
What’s included: We organize and model your operational data into a clean context layer any agent or LLM can query; build deterministic guardrails so agents behave predictably (not vibes-based); and stand up a QA + evaluation harness that proves accuracy before anything reaches users. Platform-agnostic — works with Databricks, Snowflake, or your existing warehouse, not locked to one vendor.
Who it’s for: Teams who built an AI prototype that demos well but isn’t trustworthy enough to put in front of customers.

Ongoing partnership Continuous evaluation

The continuous evaluation pipeline

Outcome delivered: You know your AI is still working tomorrow — automatically, every time anything changes.
What’s included: Automated eval suites wired into your CI/CD, regression detection on model and prompt changes, and dashboards that flag drift before your users do. The “tests for AI” layer that turns a fragile demo into something you can change without fear.
Who it’s for: Teams already in production who are flying blind on quality and afraid to touch what works.

Start anywhere — most teams begin with the foundation or the full build, then move to continuous evaluation as an ongoing partnership. Tell us the outcome you need and we’ll shape the engagement around it.

How it works

AI does the building. We do the deciding.

01 We judge
Scope the outcome

We sit with you, find the real problem, and define a concrete finish line — what “done” looks like and how we’ll prove it.
02 We judge
Architect the approach

We design the data model, the platform choices, and the plan. This is the part that needs taste and experience — so it’s ours, not the model’s.
03 AI executes
Build at machine speed

AI does the heavy lifting — writing the migrations, pipelines, transformations, and tests — under our direction and review, far faster than a traditional team.
04 We judge
Verify & hand over

We validate the result against the finish line, harden it for production, and hand you working data work plus the docs to run it. Then we tell you what to build next.

Founders

Two principals. No handoffs.

You work directly with the people doing the thinking. No bench, no junior team to manage, no account layer between you and the work.

Peter Tamisin

Co-founder & Principal

Peter has spent 20+ years building enterprise data and AI systems — from hands-on data engineering on platforms like Spark, Kafka, Snowflake, and Databricks to leading the architecture and strategy behind them. He’s the kind of technical leader teams trust to untangle a hard problem and then explain it plainly.

Stewart Bryson

Co-founder & Principal

Stewart is a foundational builder of data organizations with 30+ years across enterprise cloud, data warehousing, and analytics. He’s an Oracle ACE Director, a Snowflake Data Superhero, and has gone deep on platforms from Oracle and ODI to BigQuery and Snowflake — including the unglamorous art of keeping cloud data costs under control.

Tell us the outcome you need.

One email. We’ll tell you straight whether it’s something we can deliver, roughly what it takes, and what the finish line looks like.

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