Solutions
Start from the thing you are trying to do
Tell us what's stuck. Each of the nine below is a piece of work our senior engineers do inside your team, with a defined first step and something at the end that your people own.
Ship something new
There's a thing you want that doesn't exist yet.
Build an agentic software factory
Your engineers are faster with AI agents and delivery isn't. We set up the context store, a preview of every change and the approval gates around the agents, so a request turns into reviewed, running software with a record of who approved it. It runs in your own cluster, beside your team, until it's routine, and then it's yours.
Launch a product in a regulated vertical
You're building a product for a regulated market, and compliance review is what will hold up the launch. We design and build the app, its interface, API and workflows, with approvals and an audit trail in from day one, so the questions a regulator asks already have answers when your first users arrive.
Take the AI demo to production
The model is not the hard part. We build the durability, the permissions and the evaluation around it, and put the thing in front of real users.
Fix the foundation
Something you already run is holding everything else up.
Move off the warehouse without a Friday night
Snowflake, Synapse, Teradata or SQL Server onto Databricks, in validated waves, with a cutover that has a tested way back.
Make the estate answer for itself
Auditors keep asking who can see what, and two reports disagree about the same number. We set up grants that hold, lineage that survives a refactor and an evaluation suite that scores every change, so the audit is a query rather than a week of digging.
Modernise the estate that outgrew itself
Your roadmap is waiting on systems that got you here and now take a quarter to change. Our senior engineers read what you have, tell you which parts are worth keeping, and move one capability at a time with real traffic, switching off the old path as they go.
Build to compound
The work should leave you stronger than it found you.
Keep people in charge of the agents you already run
Your AI agents are already working in production, and the question now is what they did and who allowed it. Our engineers add a view of what's running, an approval step before the actions that need a person, and one record of every run. It sits over the agents you already have, on any framework, so your team can pause one agent without switching everything off.
Leave your team able to do it without us
Our senior engineers join your team and work in your repository, on your priorities, and plan from week one for the day we leave. You get the project shipped and your own people able to carry it on, rather than a vendor you need again for the next one.
See what production is doing, and be allowed to act
A Databricks job fails overnight or the bill doubles, and nobody can say whether it matters or who's allowed to stop it. We add a layer over what Databricks already reports that ranks alerts by rules you write, traces spend to the jobs behind it, and lets the person who notices a problem act on it.
Where to start, by what you'd say on the first call
Most people arrive with a sentence rather than a category. These are the sentences we hear most, with the engagement each one usually turns into, the first piece of work and the published work behind it.
01
"We need to get off Snowflake, Synapse or Teradata, and the date keeps moving"
Move off the warehouse. It starts with a two-week, fixed-fee assessment that inventories the estate and ends in a wave plan and a number. Location Services moved off Synapse and SQL Server this way, in reversible waves.
02
"The AI demo is great and nobody will let it near customers"
Take the AI demo to production. The prototype stays. The first work is the half nobody demos, durability, permissions and a scored evaluation set, built underneath what you have. Auto Approve is the published example of an automated decision running for real.
03
"Two dashboards disagree and the auditor wants to know who can see what"
Make the estate answer for itself. Catalogue design and grants through groups first, then lineage and evaluation, so the audit becomes a query. A half-finished Unity Catalog rollout is the usual starting state, and it doesn't hold anything up.
04
"A small change takes a quarter"
Modernise the estate. Read what exists, find the seams, then move one capability end to end with real traffic before planning the rest. AmTab is the published example of the integration work, where an order placed on the store reaches the CRM and Business Central without anyone re-keying it.
05
"We're launching into a regulated market and compliance is the long pole"
Launch a governed product. One domain's changes routed through a kernel with policy before effect, so the evidence is produced as the product runs. SWBC is the lending example.
06
"Our engineers are faster with agents and delivery isn't"
Build an agentic software factory. The context store goes in first, because search over your own tickets and decisions is useful before any agent runs. Fabric is the system we run our own delivery on.
07
"Several agents are running and nobody can say who approved what"
Keep people in charge of the agents. A mission console, approval tickets and a runs ledger over the agents you already have, whatever they're built on. Canvass keeps a required human approval inside its workflow.
08
"A job failed overnight and the person who noticed couldn't fix it"
See what production is doing. Signals from what Databricks already emits, severity set by policy, and interventions somebody is allowed to approve. It works on estates we didn't build.
09
"We need senior people inside our team, not a vendor"
Leave your team able to do it without us. Engineers in your repository on your priorities, starting from one engineer on one problem if that's what fits, with the exit planned from the start.
When two of these apply
They often do, and the order matters more than the choice. A migration and a governance problem usually travel together, and the migration's assessment comes first because governance gets carried across wave by wave rather than retrofitted afterwards.
An AI demo that needs production and a team that wants agents in delivery are separate pieces of work, and the demo is the faster win. If several agents are already live, put the console over them before building another, since every new agent makes the oversight question harder.
Several pieces of work have a fixed price and length before anyone has read your estate. The Migration Readiness Sprint, the AI Readiness Assessment, the AI Tools Assessment and the Databricks Health Check, for example, each run two weeks at a fixed fee and end in a written plan you keep whether or not we do the rest. Everything else is scoped after one of those, or after a conversation if you already know what you need.
Useful to have before the first call
- The one system or process that hurts most, by name
- What's already been tried, and why it stopped
- Who owns the outcome, and who signs off access
- A date that matters, and what happens if it slips
- Whether Databricks is already in place, being chosen, or not in the picture