AI Development Services
AI Development built for production — not the demo.
We build LLM and machine-learning systems end to end: model selection, retrieval over your own data, evaluation you can trust, and the guardrails, latency budgets and cost controls that decide whether a prototype ever ships.
- Retrieval over your own data
- Evals before every release
- Monitored after launch
The hard part isn't the model. It's everything around it
Anyone can wire a prompt to an API and get a convincing demo. Shipping is a different job: grounding answers in your own data, measuring quality before users see it, and keeping the whole thing fast and affordable at real volume.
So we start with the evaluation set, not the model. Once there is a way to score answers on your own examples, choosing between GPT, Claude, Llama or a fine-tuned smaller model becomes a measurement rather than an opinion — and every change after that can be defended with evidence.
The rest is ordinary engineering done carefully: chunking and embedding your documents, an index that stays in step with the source, caching and routing to hold latency and spend in check, guardrails on what goes in and what comes out, and tracing that shows why a given answer came out the way it did.
What ships alongside the model
Retrieval pipelines over your own documents
One-command re-indexing when the source data changes
Latency and token-cost budgets set per endpoint
Input and output guardrails, plus PII handling
Evaluation sets built from your real queries
Request tracing, quality and spend dashboards
APIs and SDKs that drop into your existing stack
The decisions that separate a demo from a system people rely on
Most AI projects don't come apart on model quality. They come apart on retrieval that returns the wrong passage, an eval suite nobody trusts, or a bill that grows faster than usage.
These are the areas we work through with you before anything reaches your users — and revisit every time the data, the traffic or the model underneath it changes.
Getting retrieval right: how documents are chunked, what actually gets embedded, whether the index is hybrid or purely semantic, and how stale results are kept out of the context window.
Ingestion you can re-run on demand — connectors to your sources, incremental updates, and a re-index that doesn't take the system offline.
Guardrails on both sides of the model: input validation and prompt-injection defense, output filtering, PII redaction, and a clear boundary around what is allowed to leave your environment.
Model choice made empirically — hosted frontier models, open-weight models such as Llama, or a fine-tuned smaller model, picked on your eval set rather than on reputation.
Evaluation and observability: graded test sets drawn from real queries, regression runs on every prompt or model change, and tracing that shows the retrieved context behind each answer.
Human review where it matters — escalation paths for low-confidence answers, feedback capture in the product, and a loop that turns corrections back into eval and training data.
Cost and latency treated as design constraints: caching, model routing, streaming and batching decided up front rather than discovered on the first invoice.
The tooling underneath everything we ship
No single framework covers an AI system end to end, and the fashionable choice is often the wrong one. This is the toolchain we reach for and the reason each piece is there — swapped out whenever your constraints call for something different.
Prompt and Context Engineering
Prompts versioned in the repo next to the code that calls them, outputs constrained to a schema so downstream services can parse them, and context windows budgeted so the model gets what it needs and nothing more.
Orchestration and Agent Workflows
LangChain and LlamaIndex where they earn their place — tool calling, multi-step chains, retries and fallbacks — and plain application code where a framework would only add indirection.
Deployment and Serving
Streaming endpoints, autoscaled inference, staged rollouts, and the ability to roll a prompt or a model version back as quickly as a code change.
Model Integration
GPT, Claude and Llama behind a single interface, with TensorFlow and PyTorch for models we train ourselves — so changing provider is a configuration change, not a rewrite.
Evaluation and Monitoring
Offline eval suites that run in CI, online quality signals from live traffic, and dashboards for latency, token spend and failure modes broken down per endpoint.
Vector and Data Infrastructure
Vector databases for semantic search, hybrid keyword-plus-embedding retrieval for the queries pure similarity gets wrong, and Apache Spark or Flink when the pipeline outgrows a single job.
Scale and Cost Control
Caching at the prompt and embedding layer, cheap requests routed to smaller models, and offline work batched — the levers that keep unit cost flat while usage climbs.
Vetted by the vendor, not just by us.
A partner badge is granted, not claimed. These came from Odoo.
- Odoo Partner
- Odoo Certified Consultants
How we work once the demo is over
Most of the cost and most of the risk in an AI system show up after launch. Our delivery model is built around that: quality you can measure, trade-offs argued in the open, and a system your own engineers can operate.
Engineers, not an account layer
The AI engineers, solution architects and product specialists who design the system are the ones who build it — no handoff to a second bench once the contract is signed.
Evaluated before it ships
A graded eval set drawn from your real queries, re-run on every prompt and model change, so quality regressions are caught in CI rather than by your users.
Trade-offs made in the open
Weekly demos against working software, architecture decisions written down along with the options we rejected, and full visibility from discovery through deployment.
Handed over, not held hostage
Your repositories, your cloud accounts, your model keys — with runbooks and documentation written so your team can operate and extend the system without us.
Bring us the hard part of your AI roadmap.
Tell us what you are trying to build and where the current attempt is breaking. We will come back with an architecture, the trade-offs behind it, and an honest view of what is worth building first.
Start a Technical Conversation
Tell us what you're
trying to build
The problem, who it's for, and where you are now. That's enough for us to come back with something useful.
hello@aryavos.com
HQ
Noida, India & New York, USA
What happens after you send it
A developer reads it, not an auto-responder. Expect questions about scope and constraints first, then a suggested way to start.