The foundation under your AI features
One North American SaaS leader hand-deployed its cloud infrastructure, with no version control and no failover plan. Rebuilt as a governed foundation, 100% of platform services moved behind private endpoints.
AI built into your product surface and the stack your teams ship on.
So the AI feature you ship in Q3 still works in Q1, at scale, for every customer.
The demo was fine. Then real volume hit. One customer's data showed up in another's answer, the model bill could not be traced, and quality drifted with nobody watching. We build the foundation that stops all three.
When our team ran into an integration issue, Architech turned it around the same day. When we needed a technical change to match our own app, they priced it and shipped it in two days. That's the kind of partner you want behind a production healthcare deployment.
Scale reaches the foundation, the churn score, and the support queue before your roadmap does.
Customer health and churn signals
At a North American SaaS leader, customer-success morning prep ran up to 90 minutes, and nobody could prove the churn score predicted churn. The redesign identified 5 signal pipelines to feed a score the team could trust, demonstrated ahead of rollout.
Support and internal operations
Tickets triaged and routed on arrival. Renewals and account risks flagged before the quarter turns. Quote to cash moving without the wait.
Technology work that is running today.
Rebuilding a Foodservice SaaS Platform's Foundation for AI
Foodservice Technology CompanyRunning live
Manual Azure deployments and disconnected partner reporting replaced with a governed, multi-region Azure footprint and a production Microsoft Fabric data platform, AI-ready from day one.
100%of platform services moved behind private endpoints
Redesigning a Customer Health Score a SaaS Team Had Stopped Trusting
North American SaaS LeaderProof of Value
A customer health score the Customer Success team had quietly stopped believing. Four weeks of workflow discovery found the five structural reasons why, and produced the redesign to fix them: the signals it was blind to, a score that explains itself, and a way to prove it against real churn before anyone is asked to trust it.
5signal pipelines identified to feed a churn score blind to risks already in the stack
Why it holds up in technology.
Foundations before features
Evaluation, keeping each customer's data separate, and cost visibility are set before the first user-facing feature ships. Every feature after that inherits them.
Your team owns it after handoff
Your engineers work beside ours through the build, not after it. When it ends, the architecture, the evaluation sets, and the patterns are theirs to extend.
Whatever the sector, the delivery model is the same.
It runs in your systems, not ours
The code lives in your repository and your cloud, whether that is Azure, Google Cloud, or AWS, behind your own access controls. There is no Architech platform to lock into.
Controls built into the workflow
A person signs off where it matters, access is scoped by role, and every action is traceable. The AI Foundations Blueprint proves how AI reaches your data before any build begins, so your security lead sees the pattern before code is written.
Measured against your KPI, on a clock
Outcome Assurance ties the system to the business metric the workflow was built to move, not to model scores. From the start of a Transformation Blueprint, the first workflow is typically live in 5 to 7 weeks. If the KPI does not move, the workflow is not done.
What technology leaders ask before starting.
- We already have a platform team. Why bring in Architech?
- For the part your team has not done before under load: the evaluation sets, the isolation pattern, and the cost tracing.
- Can you trace the model bill back to a customer?
- Yes, from the first build. Usage is tagged by customer and by feature, so cost sits next to the account it came from. Pricing a new AI feature stops being a guess.
- We shipped an AI feature already and it is drifting. Do you start over?
- No. First you get the checks you do not have: real examples the feature must keep passing, re-run on a schedule. What that finds decides whether it is repaired or rebuilt.
Free · About 2 minutes
What is the status quo costing you?
Pick the workflow that matters most and set the sliders to how the work runs. Every figure is built from your own numbers, not our projections.
No savings promises. A structural read on how much of one workflow's annual cost sits in the steps AI typically touches.
Open the cost calculator
Built entirely from your own numbers. The diagnostic later opens pre-filled with what you set here, so nothing is asked twice.
What you get
- A dollar figure for what one workflow costs to run for a year, at your settings
- The cost of waiting, projected over the months until you act
- The share of that cost sitting in the steps AI typically touches: sorting, routing, looking things up, and drafting
- A one-page summary you can download and forward to a CFO or CEO
- A direct hand-off into the diagnostic, pre-filled with what you set
Tell us what's slowing your operation down.
Name the part that costs too much or takes too long. We come back with where AI can move the number and how an AI Jumpstart would scope it.
For Technology operations.
Prefer to talk to an engineer first?
Talk to an AI EngineerRedesign the technology workflow that matters most.
Three weeks, executive-led, paid. The AI Jumpstart ends with the two or three places AI would pay for itself, what each is worth, and a clear go or no-go. If one moves into a Transformation Blueprint, the Acceptance Guarantee applies: criteria agreed in Week 1, no fee if the Blueprint's outcomes are not accepted.
