AI features that survive contact with real users.
We take generative AI work from notebook to production: RAG pipelines, fine-tuned models, evals, guardrails and the operational plumbing your team will actually own.
Building a convincing AI demo is no longer the hard part. The hard part is shipping a generative AI feature that stays accurate under real prompts, returns answers fast enough for paying users, behaves safely on sensitive data and costs something your finance team will sign off on. That is the work we do — AI engineering for teams that have a product to defend, not a pitch deck to fill.
On the LLM side we design retrieval-augmented generation pipelines on pgvector, OpenSearch or managed vector stores, choose a sensible mix of OpenAI, Anthropic and open-weight models, and add the parts most teams skip on the first attempt: evals, tracing, prompt versioning, PII redaction, fallbacks and per-tenant cost and rate limits. When data residency or latency rules it out, we deploy on-prem or hybrid with NVIDIA Triton and quantized models.
Beyond chat interfaces we ship computer vision and document intelligence pipelines for teams buried in PDFs, claims, contracts and scans. Every engagement ends with a system your engineers can read, dashboards your leadership trusts and a concrete roadmap for the next iteration — never a black box that only we can keep alive.
AI features that survive real users.
Most GenAI prototypes never make it past the demo. They hallucinate on edge cases, leak PII, blow the cost budget on launch day, or quietly drift in quality once real prompts start flowing. The gap between an impressive notebook and a production feature is where almost every team gets stuck.
Production AI requires the same discipline as any other backend system — plus a few habits that are unique to LLMs. Retrieval pipelines need evals before they need fine-tuning. Prompts need versioning. Outputs need guardrails. Cost and latency need per-tenant ceilings. Without those, every release is a coin flip your support team has to absorb.
We've shipped RAG, agentic workflows, computer vision and document intelligence systems for teams in fintech, retail and healthcare. Every engagement leaves you with the evals, tracing and rollback paths your engineers need to keep iterating safely — never a black box only we can run.
+22%
conversion lift
Real revenue impact from RAG-powered search and personalization features.
180ms
P95 latency
Production-grade retrieval pipelines that hold up under peak traffic.
60%
cost reduction
Smarter model routing, caching and quantization without sacrificing quality.
12 wks
to GA
From kickoff to a live feature with evals, monitoring and rollback paths in place.
- RAG architectures with vector databases
- Fine-tuning, evals & prompt engineering
- LLMOps: tracing, guardrails, cost controls
- Computer vision & document intelligence
- Secure, on-prem and hybrid deployments
OpenAI
Anthropic
LangChain
LlamaIndex- 01
Week 1–2
Discover
Audit current state, agree on outcomes and constraints.
- 02
Week 3–6
Design & pilot
Build the paved road on a real workload, not a demo.
- 03
Week 7–12
Roll out
Scale to teams, transfer ownership, document everything.
The questions teams actually ask.
Do we need our own model or can we use OpenAI / Anthropic?
For 90% of use cases, frontier APIs (OpenAI, Anthropic, Google) are the right starting point — they're faster to ship and cheaper at low volume. We move to fine-tuned open-weight models when data residency, latency, cost-at-scale or vendor lock-in genuinely justify it.
How do you prevent hallucinations?
Retrieval grounding, structured outputs, output validators and per-step evals catch most issues before they reach users. For high-stakes flows we add human-in-the-loop review and confidence thresholds that route uncertain answers to fallback paths.
What about data privacy and PII?
We design redaction, tenant isolation and prompt logging controls from day one. Where required we deploy fully on-prem or in your VPC with self-hosted models on NVIDIA Triton — no data ever leaves your perimeter.
How do you measure if the AI is actually good?
Every project ships with an offline eval harness (curated queries + golden judgements) and online metrics (CTR, task success, user feedback). You see quality trend lines per release the same way you'd see latency or error rates.
Book a free consultation with our CTO
Book a free consultation with our CTO to discuss your goals, assess your requirements, and determine the best path forward for your project.
Book a call