AI Engineering & Software Architecture
Complex Products powered by AI, Engineered to scale
TriVec Labs helps engineering teams transform complex product workflows into AI powered distributed systems designed for scale, reliability, and operational excellence.
Capabilities
What we build
AI - 0 to Million Users
- RAG Pipelines
- Agentic workflows
- LLM gateways
- Centeralised Eval functions
- MCP orchestrations
- Jailbreak resistance
- Observability
- Performance assessment as first class concern
Distributed Systems & Platform Architecture
- Event-driven systems
- Distributed stateful architecture
- APIs as reusable platforms
- Logical clocks and Lamport timestamps
- Cloud-native services
- High-throughput, low-latency system design
- Cost-aware architecture and infrastructure optimisation
Product Engineering
- Streamlined product workflows
- Automated recovery and self-correcting failures
- AI-powered product and operational insights
- Controlled experimentation and A/B validation
- Multi-versioned product campaigns
- Less friction and reduced organisational inertia
Architecture philosophy
Prototypes are easy. Reliable systems are not.
A demo that answers a question well is a weekend of work. A system that answers millions of questions with predictable latency, constrained cost, and graceful failure is an engineering project.
The gap between the two is where we operate.
[In modern systems, every customer query flows through multiple layers of orchestration, retrieve data from the proprietary knowledge base, invokes LLM for context aware enrichment and serves the results to customers
What distinguish a successful query from a failed one is not just the business logic, but the engineering concerns that cut across the entire system: observability, security, evaluation, cost, reliability, and latency. These are not features to bolt on at the end. They are foundational concerns that must be designed into every layer, service boundary, and control plane from the start.]
Applications
web · mobile · internal tools
Service Orchestration
hierarchal · services
Data Retrieval
search · grounding
Data Transformation
normalisation · enrichment
LLM Gateway
routing · failovers · budgets
Proprietary Knowledge Base
RAG · semantic cache · harness
Credibility
Effective Engineering, not AI slop.
Our work focuses on architecture that can actually be implemented, measured and operated.
Architecture
Designs that are backed by sound principles.
Engineering
Built by the people who designed it.
Evaluation
Every claim backed by a measurable result.
Observability
If it isn't instrumented, it isn't finished.
Reliability
Deterministic performance at scale.
How we work
Discipline, not marketing claims
We don’t sell transformation. We engineer dependable systems with measurable outcomes that are aligned to your business goals.
Production-grade by default
We design for production from the outset, with explicit contracts, failure modes, and observability embedded in the system from day one.
Pragmatic AI adoption
We adopt LLMs where they add measurable value and say so where they don't. Our experiments are backed by engineering discipline, not hype.
Experiments with discipline
Bold ideas, evaluated like engineers: Hypotheses first, experiments second, decisions by data not by enthusiasm.
Boring where it should be
The most reliable systems are uneventful. We invest in the unglamorous parts like caching, indexing, access patterns, retries, idempotency, deduplications, Wich make everything else click.
Complex system. Clear engineering.
Tell us what you're building. We'll help shape the architecture, discuss the trade-offs, build the system and make it production ready in no time.