Fewer lock-in traps
Interfaces that let you swap models when pricing or quality shifts.
AI · AI Tech Stack
An AI stack you can maintain and evolve.
Models, orchestration, vector search, evals, and app integration - chosen for clarity and cost, not trend chasing.
Outcomes
What success looks like.
App + AI coherence
Mobile/web clients, APIs, and ML services designed as one system.
Ops from day one
Logging, secrets, and environments that won't embarrass you later.
Capabilities
How we deliver in this area.
Model selection
Hosted LLMs, open weights, or classical ML - matched to the job.
Orchestration layers
Tool calling, agents, and workflow engines when needed.
Retrieval infrastructure
Embeddings, vector stores, and chunking strategies that work.
App SDKs & APIs
Clean contracts for iOS, Android, Flutter, and web.
Security baselines
Keys, tenancy, and data paths reviewed early.
CI for AI
Eval gates alongside normal software tests.
How we run it
A clear path from brief to release.
Seniors stay close to the work. Status stays honest. The process bends to your stage.
01
Constraints first
Latency, privacy, budget, and team skills.
02
Reference architecture
A diagram your engineers can build against.
03
Thin vertical slice
One feature through the full stack.
04
Harden & document
Runbooks so the system survives team changes.
Questions
Straight answers before you commit.
Will you force us onto a specific cloud?
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Will you force us onto a specific cloud?
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No. We align with where you already host - or recommend a default if you're greenfield.
Can our team own the stack later?
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Can our team own the stack later?
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That's the goal. We document and train so you're not permanently dependent.