AI for Software Engineering
Take engineering organizations from buying licenses to genuinely shipping with AI, including the hard cases: stored procedures, monolithic data models, and the undocumented systems no one wants to touch.
AI-SDLC →Thought leadership for AI-era IT
How IT teams effectively adopt AI in their operations.
Tech Teiv is a strategic technology firm. We help IT leaders, primarily in financial services but not exclusively, navigate what AI adoption actually requires: the engineering practices, the architectural foundations, the governance posture, and the talent model. We don't sell bodies. We bring thought leadership and hands-on capability in lean, fit-for-purpose teams.
Four pillars
The shape of AI adoption inside an IT organization.
Each pillar is a discipline, not a deliverable. Most engagements blend two or three. The starting point depends on where your organization actually is, not where the slideware says it should be.
For financial institutions, the underlying brief is often legacy modernization or post-merger platform integration, with the pillars applied in service of those programs.
AI Governance
AI-first architecture and controls aligned with MAS AIRM and adjacent regimes, so the AI decisions your firm puts into production are defensible, auditable, and actually accountable.
AI Governance →Portfolio Intelligence
Instrument your application portfolio to be discoverable by AI agents and capable of real-time impact analysis: the precondition for any modernization, post-merger integration, or platform consolidation that does not stall in month four.
Portfolio Intelligence →Talent for the AI Era
Hire the engineers who will actually work this way. Rebuild interviews around the questions that matter now (the last agent they built, the tasks they automated), not whiteboard rituals from 2014.
Talent →How we work
Lean teams. Senior judgment. No headcount theater.
We are deliberately different from vendors who scale by deploying bodies.
01
Understand the client need first.
No engagement starts with a staffing plan. It starts with the actual problem, and the question of whether we are the right firm for it.
02
Bring a lean team of the right shape.
Sometimes that is one person. Sometimes it is a small mixed team: fractional CIO/CTO leadership paired with current AI engineers fluent in agentic-centric development, prompting, security, and spec-driven workflows.
03
Customize the engagement model.
Thought leaders on retainer for consulting. A few sprints of R&D to push a specific concept forward. Embedded program leadership for a quarter. Whatever fits the question.
Open for business
Based across Chicago, Singapore, and Kuala Lumpur.
We work across markets and time zones, with deep roots in regulated financial services in both North America and Southeast Asia.
Chicago United States
Singapore Singapore
Kuala Lumpur Malaysia
If you are figuring out the AI question for your IT organization, we should talk.
A short conversation is usually enough to know whether an engagement makes sense. There is no minimum scope and no obligation.
Start a conversation →