For Founders and product owners building a product
Product development
We build complete products: multi-tenant architecture, authentication, billing, the operator console and the data layer underneath, on Azure, AWS, Google Cloud or Cloudflare. This is the service line where our accelerators do the most direct work, because the hard foundations are already built and running.
The hard parts are already built
Full product delivery: multi-tenant architecture, operator consoles and the data layer under them. On Azure, AWS, Google Cloud or Cloudflare, and as a Databricks App where the product belongs next to the lakehouse.
Multi-tenant platforms, billing, authentication, operator consoles, and the data layer underneath. This is the line where our accelerators do the most direct work: they compress the timeline because the hard parts are already built and running.
- Multi-tenant architecture with isolation enforced in the data layer
- Operator consoles and admin surfaces alongside the customer-facing app
- Auditable state changes where every mutation explains itself
- Accelerators cut months off the foundation
One delivery system
Fabric is the fulcrum of every TechFabric delivery team.
On a product build that means the same engineers carry a feature from the first architecture call to the release that ships it, with the agent harness doing the repeatable part of the work in between.
The engineers who scoped it are the ones who ship it.
No handoff from a solution architect to a delivery team that was not in the room. The people who made the architecture calls are accountable at go-live and through the release after it.
Agents carry the repeatable half.
Migrations, test coverage, API clients, the second and third screen that follow the pattern of the first. Our engineers review all of it, and the judgment stays with them.
Your product starts on a running system.
Fabric brings the agent harness, the logging and the governance controls with it, so week one is a working pipeline rather than an empty repository and a month of scaffolding.
What we bring with us
Systems we have already built for this work.
How an engagement works
01
Talk to an engineer
A real conversation about your initiative with a senior engineer who has built this before. Not a sales call. What you are trying to build, what has been tried, and what is realistic.
02
Discovery and scoping
Two to three weeks to clarify requirements, evaluate where AI fits, and define realistic scope. On AI work this is also where success gets defined precisely enough to score, because a goal nobody can measure cannot be hillclimbed. You get a plan you can act on before committing to a larger engagement.
03
The right team, daily demos
We put the team the work actually needs on it and show you running software every day. Built with the same rigor as any enterprise system: tested, monitored, documented.
04
Production and beyond
Deployed and running under real load, handling real business processes. Ongoing support and team continuity for whatever comes next.
Related expertise
Expertise pages that sit under this line.
Migrations to Databricks
Off Snowflake, Synapse, Teradata and SQL Server, onto Lakehouse and Lakebase, with a cutover you can reverse.
Data engineering
Pipelines that hold, tables people trust, and a bill that stops surprising you.
Data science & AI
Context stores, memory, retrieval and governed agents that survive production.
AI/BI & Genie dashboards
Genie answers a business question in English, and the answer holds up when somebody checks it against finance.
Machine learning
Models that reach an endpoint, get retrained on a schedule, and can be rolled back by somebody who was not there.
Data & AI governance
Unity Catalog designed so grants hold, lineage survives a refactor, and an agent inherits permissions instead of routing around them.
Forward-deployed teams
Product, design and engineering people who sit inside your business, find the real problem, and ship it.
APIs & durable systems
Long-running operations that survive restarts and partial failure. Temporal under the lakehouse jobs, agent runs and approvals that must not half-complete.
FAQ
Product development, answered
What is included beyond the customer-facing application?
The parts that decide whether a product is operable: an admin and operator console, tenant isolation enforced in the data layer, an audit trail of state changes, and the deployment path that gets releases out repeatedly.
How do the accelerators shorten a platform build?
TechFabric Platform gives you a governed mutation pipeline, so every state change is policy-gated and auditable from day one instead of being retrofitted. Harness gives you a durable agent runtime. Those are months of foundation you do not have to write.
Do we own the code?
Yes. Everything we build for you is yours. Where an accelerator is involved we are explicit about which parts are ours and what it means for you to keep running them.
Which cloud do you build on?
Azure, AWS, Google Cloud and Cloudflare, chosen for the product rather than for us. Where the product belongs next to a lakehouse it ships as a Databricks App running under its own service principal, and that is one option rather than the default.
If you are already committed to a provider, that is a constraint we design to rather than an argument we will have.
Can you take over an existing product?
Yes, and we build new ones from scratch just as often. Greenfield and brownfield are both normal work here: a product that does not exist yet, or a system that does and needs extending, replatforming or rescuing.
Discovery assesses what is already there before anyone proposes replacing it, so whether you extend or start fresh is decided on the evidence in front of us.