AI & MLOps
“Build Production AI That Actually Ships”
Most AI projects stall in the gap between a promising notebook and a system you can put in front of real customers. AzeniQ lives in that gap. We take agents, retrieval pipelines, and models from first prototype to hardened production, with the evaluation harnesses, observability, and cost controls that keep them trustworthy once real traffic arrives, and we tune every model for price-performance instead of leaving spend to chance.
Demos are easy. Production is the hard part.
Almost every company now has an AI pilot. Far fewer have AI in production. The demo that wowed the boardroom tends to fall apart the moment it meets real users, messy data, awkward edge cases, a latency budget, and a finance team asking why the inference bill doubled overnight. The hard part of AI was never the prototype; it is everything that comes after it.
Getting from a clever notebook to a system you can depend on means treating models like the production software they are: versioned, evaluated, monitored, and bounded by guardrails. That discipline is what separates an AI experiment from an AI capability, and it is exactly the part most teams have never had to build before.
How we get you to production.
We start where the risk is highest. Before we write production code we define what good looks like in numbers, accuracy, latency, cost per request, hallucination rate, and build an evaluation harness that measures it on every change. That harness becomes the contract: nothing ships unless it moves those numbers in the right direction.
From there we engineer the full backbone around the model. Retrieval is tuned and re-ranked rather than bolted on, agents are scoped to the exact tools they are allowed to touch, and observability tells you per route what each model is costing and how it is behaving. When a cheaper model can do the job, we route to it; when one starts to drift, you know before your users do.
Everything you need, engineered to production standards.
We take AI from a promising prototype to hardened, observable production. Agents, retrieval pipelines, and the MLOps backbone your system needs to stay accurate, cost-controlled, and reliable as usage compounds.
- From proof-of-concept to production, with success metrics defined before we write a line of code
- Bedrock Agents for single & multi-agent architectures with custom tools and guardrails
- Advanced RAG using Snowflake Cortex, OpenSearch, and Kendra, with chunking, re-ranking, and eval harnesses
- Pipelines via LangChain, LlamaIndex, Haystack, and Strands
- MLOps with MLflow, Airflow, Kubeflow, and SageMaker
- LLM evaluation, prompt & version management, and drift / hallucination monitoring
- Fine-tuning, distillation, and model routing tuned for price-performance
- Inference cost & latency observability per model and route, with budget alerts and automatic fallback
“Faster time-to-value, lower inference costs, and AI you can audit and defend.”
We engineer for production from day one, then transfer ownership so the capability stays with your team.
Built for the problems you're actually facing.
- 01 Customer-facing assistants and copilots grounded in your own data
- 02 Multi-agent workflows that take real actions behind guardrails
- 03 Internal knowledge retrieval across documents, tickets, and wikis
- 04 Rescuing a stalled proof-of-concept into a monitored production service
The stack we reach for.
Battle-tested tools, chosen to fit your team and constraints, never technology for its own sake.
- Amazon Bedrock
- SageMaker
- LangChain
- LlamaIndex
- Snowflake Cortex
- OpenSearch
- MLflow
- Airflow
- Kubeflow
Why teams pick AzeniQ for this.
Evaluation before deployment, always
We don't ship AI on vibes. Every system comes with an eval harness that scores accuracy, cost, and hallucination on each change, so quality is measured rather than hoped for.
Cost engineered in, not discovered later
Per-model and per-route observability, model routing, and budget alerts make your inference bill a design decision instead of a month-end surprise.
You own it when we leave
We build on your accounts with your engineers in the room, and hand over the runbooks, pipelines, and know-how so the capability stays in-house.
Engagements designed to leave you stronger.
Every service follows the same disciplined path: de-risk fast, engineer for production, then transfer ownership.
Frame & de-risk
We pressure-test the goal, define measurable outcomes, and ship a focused proof-of-concept fast.
Engineer to production
Hardened, observable, cost-aware systems built on AWS/Azure/GCP with security by default.
Transfer & scale
We embed the practices and mentor your team so the capability stays in-house.
Answers before you ask.
We have a proof-of-concept that stalled. Can you take it to production?
That is the most common way clients come to us. We audit what exists, define the success metrics it was missing, and rebuild the parts that won't survive real traffic, evaluation, guardrails, observability, and cost controls, without throwing away the work that is already solid.
Do we have to use a specific model or cloud provider?
No. We are model- and vendor-agnostic. We recommend what fits your accuracy, latency, and budget targets, and design so you can swap models or routes later without a rewrite.
How do you keep AI from hallucinating or going off the rails?
Two ways. Grounded retrieval with re-ranking ties answers to your data, and guardrails plus continuous evaluation catch drift and bad outputs before your users do.
How long until we see something working?
We deliberately ship a focused, measurable proof-of-concept early, usually within the first few weeks, so you can judge the value before committing to the full build.
More of what we do.
Ready to Build The Future Together?
Tell us where you're headed. We'll map the fastest secure path from idea to production.