pdelabs logo
    sun

    AI agents that make it to
    production.

    RAG systems, agentic loops and autonomous agents — engineered, evaluated and shipped.

    Schedule a call now

    whale-tale
    Anyone can wire a chatbot to an API. The hard part is everything after the demo: retrieval that holds up on messy documents, agents that recover from their own mistakes, budgets that keep the bill sane, and evals that tell you the moment quality slips.

    That is the part we do. RAG systems, agentic loops and autonomous agents, engineered like software rather than assembled like a prompt.

    What we build

    Six things we do well, and the pieces that go into each of them.

    RAG that actually retrieves

    Answers grounded in your dataMost RAG demos fall apart the moment real documents show up. We build retrieval that survives production: hybrid search, deliberate chunking, reranking and citations — measured against a golden set instead of vibes.
    • Hybrid vector + keyword search
    • Reranking & context compression
    • Inline citations and source tracing
    • Retrieval quality benchmarks

    Agentic loops

    Plan, act, observe, correctA single prompt is not a product. We design the loop around the model: typed tools, structured outputs, bounded retries and self-correction, so the agent recovers from its own mistakes instead of confidently shipping them.
    • Typed, permissioned tool calls
    • Structured output & schema validation
    • Bounded retries and back-off
    • Deterministic control flow where it matters

    Autonomous agents

    Work that happens without youLong-running agents that hold memory, run on a schedule and act on your systems — with human-in-the-loop checkpoints exactly where the stakes justify them, and a full audit trail for everything else.
    • Persistent memory & state
    • Scheduled and event-triggered runs
    • Human-in-the-loop approvals
    • Full audit trail of every action

    Evals & observability

    Know when you break itThe difference between a demo and a product is knowing it still works tomorrow. We ship golden datasets, LLM-as-judge scoring and regression gates in CI, plus tracing on every call in production.
    • Golden datasets & regression suites
    • LLM-as-judge and rubric scoring
    • Tracing on every span
    • Cost and latency dashboards

    Guardrails & safety

    Autonomy with a seatbeltAutonomy is only useful when it is bounded. Token and action budgets, timeouts, permission scopes, prompt-injection defence and reversible operations — so the worst case is recoverable, not catastrophic.
    • Token, cost & action budgets
    • Scoped permissions per tool
    • Prompt-injection hardening
    • Reversible, idempotent operations

    LLM infrastructure

    The unglamorous part that decides the billModel routing, prompt caching, streaming, queues and graceful fallbacks. The engineering that turns a promising prototype into something that stays fast, cheap and up under real traffic.
    • Multi-provider routing & fallbacks
    • Prompt caching & token budgeting
    • Streaming and background queues
    • Rate limits, retries, dead letters

    What is inside an agent we ship

    An agent is not a prompt. It is five systems that have to hold together — and steps two to four run in a loop until the goal is met or a budget stops it.
    1. 01

      Context

      Retrieval, memory and permissions decide what the model is even allowed to see.
    2. 02

      Tools

      Typed, idempotent, scoped. Every capability the agent has is a contract you can review.
    3. 03

      Loop

      Plan, act, observe, correct — until the goal is met or a budget says stop.
    4. 04

      Guardrails

      Budgets, timeouts and approval gates keep autonomy inside boundaries you set.
    5. 05

      Evals

      Golden sets and judges gate every change, so quality is a number and not an opinion.

    Built in-house

    Hermes

    Our agent runtime for autonomous work.
    RAGagentic loopsautonomous agentstool useevalstracing
    Hermes is the runtime we built for ourselves after shipping enough agents to get tired of rebuilding the same scaffolding. It handles the parts that are the same every time — the loop, tool routing, memory, retries, budgets, tracing and evals — so a project starts at the interesting problem instead of at plumbing.For you that means agents in production in weeks, not quarters, on infrastructure that has already been through the failure modes yours is about to hit.
    Loop enginePlan/act/observe cycles with budgets, timeouts and clean cancellation.
    Tool registryTyped tools with scoped permissions and automatic schema validation.
    MemoryShort-term working context plus durable long-term recall across runs.
    RetrievalPluggable hybrid search with reranking and citation tracking built in.
    TracingEvery span, token and tool call recorded and replayable after the fact.
    Eval harnessGolden sets and judge scoring wired into CI as a release gate.

    How an AI project with us goes

    Short steps, each one ending in something you can actually judge.
    1 week

    AI opportunity audit

    We map your workflows and data, and come back with the two or three places where an agent pays for itself — and the places where it definitely will not.
    2–4 weeks

    Working prototype

    A real agent on your real data, with an eval set from day one. You get something you can use and judge, not a slide deck.
    4–8 weeks

    Production hardening

    Guardrails, observability, cost control, permissions and the integration work that makes it part of your product rather than a side experiment.

    Have a workflow you think an agent should be doing?

    Let's talk about it

    We love to take on new challenges, tell us yours.

    Schedule a call

    Or
    if your prefer taking it offline, write us
    via email
    We will get back to you in less than 24 hs.