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Everyone says they build with AI now.
But is it a habit or a process?
AI agents save time, but that only holds up when someone has defined how they work and who signs off. We built 30+ Tallium agents that follow the engineering standards and security rules we set for each project.
Yesterday
AI as co-pilot
AI in development meant a developer asking a chatbot how to approach something. That’s still the most common version of it, and the one most agencies mean when they tell you they use AI.
AI in development meant a developer asking a chatbot how to approach something. That’s still the most common version of it, and the one most agencies mean when they tell you they use AI.
Now
AI as operator
AI agents write the code within the standards our team sets, and a named senior engineer approves every merge. Routine tasks take hours instead of days, so more of your budget goes into the product.
AI agents write the code within the standards our team sets, and a named senior engineer approves every merge. Routine tasks take hours instead of days, so more of your budget goes into the product.
Get More of Your Roadmap on the Same Budget
This is the two ways we build in. Both use the same delivery process and senior engineers. With AI-First Engineering, agents take on the routine work, so the same money covers more of the build.
Who writes the code:
What engineer does:
When we recommend it:Classic Development
Who writes the code:
Our engineers, line by line. AI is available as a consultant if the project allows it.
What engineer does:Authors the work, then reviews it.
When we recommend it:When your code or data can’t be shared with an AI model because of internal policy or regulations.
AI-First Engineering
Who writes the code:
Tallium agents, working inside standards our engineers codify per project. A named senior engineer scopes and approves every merge.
What engineer does:Frames the task, sets constraints, reviews output, and owns the result. Senior engineers put their time into decisions and review.
When we recommend it:For most commercial software, where speed and coverage matter and there’s no restriction on how the work gets done.
Ready to ship faster without compromising quality?
Bring us what you’re building and we’ll walk you through our approach.
120+
In-house IT-professionals
250+
Projects delivered
15+
Years on the market
The same SDLC, run by a different
mix of people and Tallium agents
With AI-First Engineering, every project runs through the lifecycle we’ve always used. Discovery and requirements at the start, design and architecture next, then development, QA, release, and support. Agents didn’t replace any of that, and no stage was dropped to make room for them.
Discovery
& Concepts
Requirements analysis & workshops- Software requirements specs
- Work breakdown & estimate
- Technical architecture
Human buildsAI builds
Prototypes
& Design
Design concepts- App map / UX flow
- Wireframes & UI/UX designs
- Roadmap & design handoff
Human buildsAI builds
Development
& QA
Project & infrastructure set-up
(CI/CD, database)- Feature development
- Code review, debugging & staging
- Test plan, manual
& E2E automation - Performance testing
Human buildsAI builds
Deployment
& Support
Internal acceptance validation- Client demo & review (UAT)
- Production deployment & release notes
- Support, bug fixing & improvements
Human buildsAI builds
What changed is who does the work at each stage.
Decisions and work that need project context stay with our engineers.
Mechanical and well-defined parts move to agents working inside standards our team sets for the project.
What Changes for Each Role on the Project
Business Analyst
Before:- Wrote tickets and specifications by hand
- Found contradictions in requirements once development hit them
- A prototype meant a separate mini-project
Now:- Reviews AI-drafted specs and decides what’s right
- Gets gaps and contradictions flagged before work starts
- Tests an idea with a clickable prototype in a day
Business Analyst
Before:- Wrote tickets and specifications by hand
- Found contradictions in requirements once development hit them
- A prototype meant a separate mini-project
Now:- Reviews AI-drafted specs and decides what’s right
- Gets gaps and contradictions flagged before work starts
- Tests an idea with a clickable prototype in a day
Solution Architect
Before:- Drew diagrams and wrote up decisions by hand
- Weighed alternatives from memory and experience
- Checked architectural compliance by eye during review
Now:- Makes the decision, then reviews the generated diagram and write-up
- Gets alternatives and trade-offs laid out, then chooses
- Every change is checked against the architecture rules automatically
Senior Developer
Before:- Wrote code by hand most of the day
- Read every line in review
- Handled one piece of work at a time
Now:- Defines the work for agents and reviews what comes back
- Reviews logic, design, and edge cases after the first pass is done
- Runs two to four workstreams at once
QA Engineer
Before:- Hand-wrote test cases and automation
- Regression testing held up every release
- Wrote up bugs manually
Now:- Designs the test strategy and verifies the critical paths
- Validates the regression tests agents generate
- Gets a drafted bug report with steps, logs, and a likely cause
Client / Stakeholder
Before:- Waited weeks to see anything clickable
- Discovered scope gaps in the middle of development
- Chased the team for status updates
Now:- Clicks through a working prototype in the first week
- Sees risks and trade-offs before the budget is committed
- Gets clear progress summaries without asking
What AI-First Engineering Brings You
You get more out of every sprint and the product stays easy to change and hand over.
Same budget, more value
Agents handle the routine work, so more of your budget goes into the product itself. What’s included is agreed per project and written into the estimate.
Faster delivery
Development tasks complete faster and review cycles get shorter, so you’re waiting less between asking for something and seeing it.
Lower cost of change
You see working increments as they land. Changing course costs less, so you can try a few ideas before committing to one.
You’re not dependent on us
Everything gets documented as the work happens. Any team that picks up the product later has the trail to follow, so you’re never locked in to us.
But will AI code quality be any good?
We get the hesitation, so we built guardrails.
Engineer Sign-off
Agent proposes, a senior engineer reviews and approves. Nothing reaches your product without a person deciding it should.
Our Standards Built-in
Our engineering standards are written into the agent’s instructions per project, so the code is held to our bar.
Reviewed Changes
Agents work in small chunks with acceptance criteria. Automated unit tests and dedicated QA run on top of human review.
A Written Trail
Every feature carries a written trail from requirements to test. Want to know why something was built the way it was? Read it at any time.
Your IP Stays Yours
Nothing you share is retained or used to train a model. IP ownership and data handling are the same as on any classic project.
The Same Warranty
Our post-release warranty applies exactly as it does on classic delivery. If something breaks, fixing it is on us.
Questions clients ask most often
Everything you need to know about how we leverage AI-First Engineering to build high-quality software faster.
Copilot and similar assistants suggest code while a developer writes it. With AI-First Engineering, agents take on whole development tasks, and our senior engineers scope the work, set the standards the agents follow, and approve every merge. Every project gets its own agents, standards, and review gates.
A named senior engineer on our team. Agents propose the change, and the engineer checks the logic and the edge cases before anything gets merged. Nothing reaches your product without a person approving it.
AI-written code goes through the same security checks as anything our engineers write by hand. Our security rules are written into the agent’s instructions for each project, and a senior engineer reviews every change before it merges.
On standard development tasks it saves 20 to 40% of the time. Some parts of the process speed up far more than that, with automated test coverage as the clearest example. That freed-up time and budget usually goes back into the product — test coverage, documentation, and product work a tight timeline would normally cut.
Yes. It works whether we’re building something new, adding features to what you have, or modernizing something older. The agents work from your codebase and the standards we set for the project, so what they write follows existing conventions.
We work with Anthropic and OpenAI models alongside self-hosted ones, through our own harness built on OpenRouter and OpenWebUI. On top of it, we build the agents, skills, and guardrails for each project. If your organization has already settled on particular tools or a particular cloud, tell us early and we’ll work out what fits.
We build the project the classic way, with no AI in the delivery chain. It’s one of the first things we check before recommending an approach, so it’s worth raising early.
You do, exactly as on any other project with us. That includes the code, the tests, the architecture decisions, and the documentation. Nothing you share with us is retained or used to train a model.
Yes. Classic full-cycle development is still on the table, with the same team and the same delivery process. Some projects suit it better, and if yours is one of them we’ll say so.
Let’s talk about AI-First Engineering
Share the details of your AI idea and we’ll get back to you to set up a call to discuss your project
120+
In-house
IT-professionals
IT-professionals
250+
Projects
Delivered
Delivered
14
Years
on a market
on a market
