AstraCloud Labs designs and builds AI agent systems and LLM products that run in production, not just in a demo. We work with enterprise teams who have proven an idea and now need it to be reliable, observable, and safe at scale: tool-using agents, retrieval pipelines, orchestration across model providers, and the evaluation and guardrails that keep all of it honest under real traffic. The work is delivered as engineering projects, end to end, by a small senior team led by a principal engineer with eleven years of experience.
What we build
Most AI work fails the jump from prototype to production for the same reasons: no way to measure quality, no control over model output, and no visibility when something drifts. We build the parts that close those gaps, using TypeScript and Python on AWS, GCP, and Kubernetes, integrating with OpenAI, Anthropic, and Google Gemini through a single orchestration layer.
Tool-using agents
Agents that call tools, query systems, and complete multi-step tasks with typed parameters, least-privilege access, and error handling that fails predictably rather than silently.
RAG pipelines
Retrieval-augmented generation built for real corpora: chunking, reranking, and grounding that reduce hallucination, with retrieval quality measured rather than assumed.
LLM orchestration
An orchestration layer that routes across providers and models, manages prompts and state, and fails over cleanly when a provider degrades or rate-limits.
Evaluation frameworks
Offline eval sets, LLM-as-judge harnesses, and regression gates wired into CI, so a prompt or model change cannot ship without a quality signal behind it.
Guardrails
Input and output validation, prompt-injection defences, and policy checks that constrain what an agent can do and say before a response reaches a user.
Multi-provider integration
OpenAI, Anthropic, and Gemini behind one interface, with LangChain where it earns its place, so you can switch models on cost, latency, or capability without a rewrite.
If you are weighing retrieval against tuning the model itself, our note on RAG versus fine-tuning covers the trade-offs, and the production RAG pipeline walkthrough shows how we put a retrieval system together once the decision is made.
How we work
We treat AI systems as software first. That means version control, tests, code review, and deployment pipelines, applied to prompts, retrieval configuration, and agent logic, not only to application code. The model is one component in a system that has to be measurable and operable.
Evaluation as a first-class concern
Before we tune anything, we build a way to score it. Evaluation sets and regression gates turn vague reports of "the answers got worse" into a number that moves, so changes to prompts, retrieval, or model choice are decisions backed by evidence rather than impressions.
Guardrails and observability
We instrument agents with OpenTelemetry and Langfuse so every call, tool invocation, and token cost is traceable in production. Guardrails sit around the model to validate inputs and outputs and to block known attack patterns; our note on preventing prompt injection goes into the specifics. The same telemetry that helps you debug also feeds the evaluation loop.
How a project tends to run
- Start with the failure modes that matter, then build evals that catch them
- Ship a thin slice to production early and instrument it from day one
- Keep prompts, retrieval config, and agent logic in version control and CI
- Route across providers behind one interface so model choice stays reversible
- Hand over runbooks, dashboards, and eval suites, not just a codebase
Orchestration is where these pieces meet. If the term is new to your team, what is LLM orchestration explains the layer that coordinates prompts, models, tools, and state across a request.
Proof
For the British Council we build and maintain AiBC, an AI-powered English learning platform serving millions of learners. The work covers agentic tutoring workflows, LLM orchestration with multi-provider routing, evaluation, and guardrails, alongside the GDPR and data-residency constraints that come with operating at that scale. It is a representative example of taking agentic AI from concept to a production service that has to stay reliable for a very large user base.
For AllBright Collective we built a Neo4j graph-powered recommendation API serving three frontends and a mobile app. Recommendation systems sit next to this work: they reward the same engineering discipline around data modelling, serving multiple clients from one API, and keeping quality measurable.
What teams get from working with us
- AI features that survive production traffic, with evals to prove it
- One orchestration layer across OpenAI, Anthropic, and Gemini
- Guardrails and tracing built in, not bolted on after an incident
- A senior engineering team delivering the project end to end
Where this connects
AI systems do not run in isolation. The agents and pipelines we build sit on top of cloud infrastructure and DevOps work, the deployment pipelines, networking, and Kubernetes platforms that keep them running. When the data is sensitive, as it is with learner records under GDPR, our security and compliance work covers data residency, access control, and the audit trail that regulated clients need. If you have an AI product to move from prototype to production, that is the work we do.
