Technology and SaaS
Win bigger customers and keep shipping.
AI features, enterprise readiness, moves between clouds, and a bill that keeps pace with revenue.
Agent workspace · Enterprise asks
An illustration of an agent we build
Queue
- SSO setupApproved
- Audit log exportPreparing
- Data residencyWaiting
- Security reviewWaiting
SSO setup
ApprovedReads
- Customer questionnaire
- Cloud config
- Access logs
The agent prepares
- Collects the evidence
- Drafts the change
- Runs the checks
Ready for review, with every fact linked to where it came from.
A person decides
Your team approves
Shipped
- Google Cloud Partner
- Databricks Partner
100+
Projects delivered
45+
Enterprise clients
8
Industries served
98%
Client satisfaction
What we build
Build what your next customer asks for.
Pick a workflow and watch it run. Three of our case studies are software companies, shown further down.
AI features in your product
Product and engineering teams
Retrieval over each customer's own data, with an evaluation on every release.
Reads
- Tenant data
- Your product
- Evaluation set
The agent prepares · Agent Platform
- Retrieves per tenant
- Drafts the answer
- Runs the evaluation set
Hands over · Release candidate
- Tenant retrieval · Tenant 214 only
- Answer draft · With 3 citations
- Evaluation scores · Run on 400 cases
A person decides · approved
Your team ships or holds
Then
Release in production
Built on · Gemini models on Agent Platform · AlloyDB · BigQuery
Read every workflow as text
AI features in your product
Product and engineering teams
Retrieval over each customer's own data, kept separate per tenant, with an evaluation set that runs on every release and the cost of every call on a dashboard.
A person decides: Your product team decides what ships, and a release that fails its evaluation does not.
We measure: Evaluation scores per release, cost per call, and feature adoption.
Built on: Gemini models on Agent Platform, AlloyDB, BigQuery
Customer onboarding in weeks
Implementation and professional services teams
The configuration and data mapping that take months by hand run as a pipeline, with a review step, so each new customer costs less to start than the last.
A person decides: An implementation lead approves every mapping before it goes live.
We measure: Time from signature to go-live, and services hours per new customer.
Built on: Document AI, Agent Platform, Cloud Run
Isolation you can explain to any security reviewer
CTOs and security leads
Each tenant's data is separated in a way you can explain to a customer's security team, sign-on runs through their identity provider, and every customer can export their own audit log.
A person decides: Your security team approves the isolation model and every exception.
We measure: Time to clear a customer's security review, and exceptions raised.
Built on: Identity Platform, Cloud KMS, Cloud Logging
Audit evidence from the platform
Compliance and security leads
The technical evidence for SOC 2 and security questionnaires comes from configuration and logs, so your engineers stop collecting it by hand. It feeds the compliance tool you already use.
A person decides: Your auditor decides. We make the evidence easy to hand over.
We measure: Hours spent preparing for each audit.
Built on: Security Command Center, Cloud Asset Inventory, Cloud Logging
Off hand-managed servers
Platform and operations teams
Jobs that each run on their own hand-tuned server move to one platform defined in code, so adding capacity is a reviewed config change.
A person decides: Your team approves each move and its cutover window.
We measure: Manual operations per week, and deploys per week.
Built on: Google Kubernetes Engine, Cloud Scheduler, Terraform
A move from another cloud, in waves
CTOs and platform teams
Workloads move from AWS or Azure in waves planned around your release calendar. Databases replicate before cutover, and you can roll back every wave.
A person decides: You choose the order of the waves and approve each cutover.
We measure: Downtime per wave, and monthly cost against the old cloud.
Built on: Migration Center, Database Migration Service, Google Kubernetes Engine
Cost per tenant and per feature
CTOs and finance leaders
Spend is broken down by customer and by feature, so pricing and roadmap decisions start from real unit costs.
A person decides: Your finance and product leads decide pricing.
We measure: Gross margin per customer, and cost per feature.
Built on: BigQuery, Looker
Four stages of growth
Add what the next stage needs.
Step through the stages most SaaS platforms go through.
Swipe sideways to see the whole drawing.
- Users
- Load balancer
- APICloud Run
- DatabaseManaged
A guide. We build in the order your customers ask for.
Proof
Software companies we have built for.
Software for health and human services
Client onboarding cut from six months to weeks
A government and non-profit health and human services case management platform
80 to 90%
of form onboarding effort automated
Public Sector · Document AI · Google Cloud
Read the study
Software for e-commerce
Manual resource allocation cut by 80 to 90%
A high-volume CRM and order management provider
900+
data processing tasks moved off hand-managed VMs
E-Commerce · Migration · Google Cloud
Read the study
Healthcare software
Moved off a vendor data centre with zero downtime
A U.S. healthcare technology company delivering clinical analytics for providers and hospitals
8 weeks
to own and govern its own infrastructure
Healthcare · Migration · Google Cloud
Read the study
What is changing
The bill, the margin and the accuracy of AI all get checked now.
Each item links to its source, for your next board update.
85%
say managing cloud spend is a top challenge
29%
of cloud spend estimated as waste, up for the first time in five years
2025 survey
46%
of developers distrust the accuracy of AI tools
And 66% are frustrated by answers that are almost right.
Stack Overflow 2025 Developer Survey, AI section2026
52%
projected average gross margin on AI products in 2026
And 37% plan to change how they price AI within a year.
SaaStr summary of ICONIQ's State of AI, January 202612 Jan 2027
12 Jan 2027
until EU cloud switching charges end
Under the EU Data Act, apart from in-parallel use.
Regulation (EU) 2023/2854 (Data Act), Article 29
Read every item in full
2025 survey
Nearly half of developers distrust AI output.
46% of developers distrust the accuracy of AI tools, and 66% are frustrated by answers that are almost right. An AI feature needs an evaluation set before your customers see it.
2026
AI features carry their own gross margin.
Software executives in ICONIQ's State of AI report projected an average gross margin of 52% on AI products for 2026, and 37% plan to change how they price AI within a year.
12 Jan 2027
Cloud switching charges end in the EU.
Under the EU Data Act, cloud providers may no longer charge customers for switching to another provider, apart from in-parallel use. A move between clouds can be judged on its engineering.
The engineering underneath
Try to make it leak.
Pick a request your users or your team might make. Watch what the feature does, and what lands in the log.
As Tenant A user
As Support agent
As Engineer
AI feature in your product
Signed in as Tenant A user
Retrieval is filtered by tenant before the model sees anything. Documents from other tenants returned: 0.
Audit log
- filtered · cross-tenant results: 0 · tenant A
A simulation of the controls we build into AI features and agents. It is not a live system.
- Runs inside your own Google Cloud project
- A person approves what the agent prepares
- Every action written to the audit log
- Scored against your past cases before launch
Read the requests and the engineering behind them as text
Tenant A user: Show me another customer's documents
Filtered. Retrieval is filtered by tenant before the model sees anything. Documents from other tenants returned: 0.
Support agent: Delete this customer's account
Refused. No tool here can delete an account. That stays with a person, in your admin console.
Tenant A user: Where did this answer come from?
Cited. Each answer cites the tenant's own documents it used, with links back to them.
Engineer: Ship the new prompt to everyone
Scored. The release runs the evaluation set first. A lower score blocks it, and the history shows why.
Everything defined in code
It prevents: An environment nobody can rebuild because it was configured by hand. You check it: Any environment can be recreated from the repository, and we show you a rebuild.
Evaluations on every release
It prevents: An AI feature that quietly gets worse when a prompt or a model changes. You check it: Each release runs the test set and blocks on a failing score. You see the history.
Staged rollout and one-step rollback
It prevents: A bad deploy reaching every customer at once. You check it: Every merge ships through tests and a staged rollout, and rollback is one command we rehearse.
Load tests from your real traffic
It prevents: Finding the platform's limit during your biggest customer's launch. You check it: You get the test plan, the results and the capacity rules before each stage goes live.
How we start
We begin with the customer you're trying to win.
Most software companies start with one blocker on one deal.
First
A call with an engineer
Bring one workflow and its systems.
Fixed fee
Discovery
A written plan, or a written no.
Day 0 to 30
Build and test
Starts once access to your project is approved.
Day 30
First piece live
Internal agents in thirty days. Product work is priced in discovery.
After
Run it, or hand it over
Your call. The next one reuses it.
We need from you
- An engineering owner
- A few hours a week from the team
- Access in your project
- Realistic tenant data to test with
What can slow it down
- Releases that can't pause: we ship in steps
- Data residency terms: read first
- Undocumented parts: runbooks as we go
You keep
- The code, in your repo
- The test set and its scores
- Runbooks and the audit log
- Your Google Cloud project
Read each step in full
A call with an engineer
Tell us the workflow and the systems it touches. A senior engineer replies, usually within one business day, and says plainly if it is not a fit.
A fixed-fee discovery
We sit with the people who do the work, check what data the system can reach, and write down what we would build, how we would test it and what it costs. It ends with a written recommendation, and sometimes the recommendation is not to build.
A first piece live, then the rest in stages
For an internal agent, one workflow goes live on Gemini Enterprise in thirty days, at a fixed fee. AI features in your own product, migrations and platform work are scoped and priced in discovery, and move in stages your releases can ship around.
Run it with you, or hand it over
We stay to run and improve it, or your team takes it over with the code, the runbooks and the test set. Either way, the next workflow reuses everything underneath.
What can slow it down, and what we do about it
- Releases that cannot pause. We work in steps your team can ship around.
- Customer contracts that fix where data lives. We read them before choosing regions.
- Parts of the platform nobody documented. We write the runbooks as we go.
Frequently asked
The questions we hear first.
Where it hurts first, usually the database, one hot service or the deploy process, and we fix that before anything else. A full re-architecture is rarely needed all at once.
Yes. Most product AI builds on existing models. We build the retrieval, the evaluation set, the guardrails and the cost tracking around your data, and hand your engineers a feature they can keep improving.
Retrieval is filtered by tenant before the model sees anything, each tenant's documents sit in their own index or partition, and the evaluation set includes cross-tenant tests that must come back empty.
No. We move what benefits from Google Cloud and connect the rest. When a full move does make sense, it happens in waves with a tested way back.
We find where spend and usage have drifted apart, right-size what is over-provisioned, and put cost per tenant and per feature on a dashboard, so cost becomes part of engineering reviews.
Every AI feature gets an evaluation set built from your customers' real questions, including cross-tenant tests that must come back empty. It runs on every release, and a release that lowers the score does not ship.
You do. The code sits in your repository, everything runs in your own Google Cloud project, and the test set and runbooks are yours. Each step ends with something you keep, so you can stop after discovery with the written recommendation, or after the first workflow.
Tell us where the architecture creaks.
A senior engineer replies, usually within one business day.