Agentic AI Consultancy

Agentic AI,
engineered for production.

We design, build, and operate multi-agent AI systems — and the LLM inference & GPU/memory economics that decide whether they're affordable at scale. Enterprise systems depth meets modern AI.

See our work Start a conversation

What we do

Four areas that make or break production agentic AI.

Agentic AI Systems

Multi-agent orchestration, RAG, tool use, evals, and observability — built to run reliably, not just demo.

LLM / GPU Cost Optimization

Profile where GPU memory goes, find the bottleneck, and cut inference cost — quantization, batching, KV-cache sizing.

Distributed Systems

Kafka/CDC streaming, idempotency, retries, back-pressure — the reliability layer agents depend on.

Enterprise & Salesforce AI

Deep experience across Salesforce, MuleSoft, and Heroku — wiring agentic AI into real enterprise systems.

Featured project

Memory Profiler

An agentic tool that profiles how an LLM uses GPU memory on real hardware and produces ranked, quantified optimization recommendations — so teams cut inference cost without an ML-perf engineer on staff.

✓Two-stage design: a GPU Profiler + an Advisor (NVIDIA Nemotron via NIM)
✓Measured 47% memory reduction (FP16 → INT8) on a real Tesla T4
✓Python, FastAPI, NVIDIA NeMo stack, deployable on Modal

View the dashboard

$ memoryprofiler advise phi3_t4_fp16.json --int8 phi3_t4_int8.json

Verdict : memory-bound; weights are 97% of peak

Headline: INT8 cuts weights 47% (7.64→4.02 GB), freeing 3.6 GB on Tesla T4

[1] Quantize FP16 → INT8 — 3.6 GB freed, ~<1% quality

[2] Optimize for memory bandwidth

[3] Re-profile at production context length

Have an agentic AI problem worth solving?

Architecture reviews, LLM/GPU cost audits, and custom multi-agent system development.

Get in touch

Agentic AI,engineered for production.