AI Orchestration Platforms
A single model call is not an AI system. An AI system is a coordinated set of models, tools, and data sources working together to complete tasks that no single model call can handle alone.
We build AI orchestration layers that coordinate models, manage state, route between specialists, handle failures, and deliver reliable outcomes across complex multi-step workflows.
LangGraph, LangChain, and custom orchestration for multi-step AI workflows
Multi-model pipelines, routing to the right model for each task
Agent memory, state management, and context window handling
Production monitoring, retry logic, and graceful failure handling
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
AI prototype works for simple cases but fails on multi-step tasks in production?
Single model call cannot handle the complexity of your workflow?
Plain answer
RaftLabs builds AI orchestration platforms: multi-model pipelines, LangGraph agent workflows, state management, and production-grade coordination with retry logic, failure handling, and monitoring.
What to remember
A ChatGPT demo that works for simple inputs breaks on real-world complexity. Documents that don't fit in context, tasks that require multiple steps, workflows where one model's output is another model's input, and errors that need graceful handling rather than full failure.
A single model call is not an AI system. An AI system is a coordinated set of models, tools, and data sources working together to complete tasks that no single model call can handle alone. Orchestration is the engineering that closes that gap.
RaftLabs has shipped production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE. One team maps the workflow, the models, and the failure modes, builds the orchestration layer, and hands it over, with your regulatory requirements designed in from the start.
Everything on the left should already be true for your workflow. Even one thing on the right, and a simple API call is the smarter first step.
A multi-step workflow where one model's output feeds the next, and a single model call already breaks on your real inputs.
Systems and data to integrate against, plus reliability requirements that make retry logic and failure handling non-negotiable.
A budget that matches a production build, and a decision-maker who can define what a good outcome looks like.
A single-step task whose inputs fit in one context window and needs one model's output.
Failure handling is not critical and a simple API call already does the job.
No workflow defined yet, and requirements still forming.
What we build
Document processing pipelines that classify, extract, validate, and route in a defined sequence: a document enters one end and structured, validated data exits into your target system at the other. Each step uses the model best suited for the task, a fast classifier for document type routing, a precise extraction model for pulling fields from variable layouts, and a reasoning model for edge cases. Step outputs are validated against business rules before passing to the next step, and exceptions surface to a human review queue with full context. Typical stack: Claude Haiku and GPT-4o mini.
Stateful AI agents that plan and execute multi-step tasks using tools: database queries, API calls, code execution, and web search. LangGraph checkpointing lets an interrupted agent resume from the last step instead of restarting. Guardrails prevent the production failure modes: bounded step counts, permission-scoped tools, confirmation before anything destructive, and escalation to a human when state is unrecoverable. Every tool call is logged, and supervisor-worker architectures parallelise tasks that benefit from it.
Orchestration that routes each step in a workflow to the model best suited for its cost and capability requirements, because sending every query to your most powerful model is like using a surgeon to file paperwork. Fast classification goes to a small model; complex reasoning goes to a frontier model. Smart routing keeps inference cost down, with accuracy trade-offs measured against your evaluation dataset, not assumed. Fallback logic switches providers when latency breaches your SLA. Routing spans Claude Haiku, GPT-4o mini, Claude Sonnet, and GPT-4o.
Retrieval-augmented generation pipelines engineered for production accuracy, not benchmark performance. Naive top-k vector search returns the most similar chunks, but similarity is not relevance. We over-retrieve 20-50 candidates, then re-rank before the top 3-5 enter context. Hybrid retrieval adds BM25 keyword search to catch exact-match terms vector search misses.
AI workflows with explicitly designed human intervention points, because full automation is not always the right architecture in regulated industries or high-stakes decisions. Low-confidence outputs route to a review queue with the document, output, and confidence score side by side. High-stakes categories require sign-off with a time-boxed SLA and escalation. Every AI decision and human intervention is logged, producing the audit trail compliance teams require. The AI handles volume; humans handle judgment.
Full observability across every orchestration step: inputs and outputs captured for every step, token usage and cost tallied per workflow run, latency measured at each node, and error types classified for root cause analysis. End-to-end dashboards show throughput, success rate, average cost per run, and step-level latency percentiles. Alerting fires when error rates spike, latency exceeds your SLA, or cost per run climbs beyond threshold, the early warnings that prevent a quiet model degradation from becoming a user-visible quality problem. Quality evaluation runs automated test sets on a schedule to catch accuracy regressions before they reach production. Instrumented with LangSmith and Langfuse.
Tell us what the workflow needs to accomplish, the tools it needs to use, and the reliability requirements. We will design the orchestration architecture.
How it works
Every AI orchestration project follows the same four phases. Scope is locked and price is fixed before development starts.
We map the workflow, the models, the tools, and the failure modes. You leave with a written orchestration spec and a fixed-price quote. No development starts without your sign-off.
We design the graph, the routing logic, the state schema, and the human-in-the-loop points before writing production code.
Working orchestration at a staging URL by the end of sprint one. Bi-weekly demos. QA runs against real workflow inputs in parallel with every sprint, not as a phase at the end.
Production deployment with full observability activated on launch day: throughput, cost per run, error rate, and latency dashboards live from day one. 8 weeks of post-launch support included.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
"I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support."
Amer Abu Khajil
Founder, Peak Studios & Perceptional
"All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!"
Charles E.
Entrepreneur at Aggie Technologies
Proof
We built a branded booking website with RMS Cloud integration and a Bluetooth keyless mobile app for City Break Apartments in Dublin, activating 250 Omnitec locks already installed, cutting 20+ staff hours per week, and growing self check-ins from fewer than 10 to 72+ weekly.
Work with us
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
AI orchestration is the coordination layer that manages multiple AI models, tools, and data sources working together in a pipeline or agent workflow. A single LLM call handles a single task. AI orchestration handles: calling a retrieval system before the LLM, routing between models based on task type, managing state across multi-step agent workflows, handling tool use results and errors, and retrying failed steps. Orchestration is what turns a demo into a production AI system.
A simple API call is sufficient when: your task is single-step, inputs fit in the context window, you need one model's output, and failure handling is not critical. AI orchestration is needed when: your workflow requires multiple steps (retrieve, analyse, generate, validate), you need to route between models based on task complexity or cost, your agent uses tools that produce results it needs to reason about, you need to maintain state across a conversation or workflow, or failures in one step need graceful fallback rather than a full error.
LangGraph is an open-source orchestration framework for building stateful AI agent workflows as directed graphs. Each node in the graph is an AI step or tool call; edges define the routing logic. LangGraph handles state management, cycles (when an agent needs to loop or retry), and parallel execution. We use LangGraph for complex agent workflows with many states, conditional branching, and human-in-the-loop requirements. For simpler pipelines, custom orchestration without a framework is often cleaner and more maintainable.
Every orchestration step can fail: API rate limits, model unavailability, tool execution errors, and unexpected model outputs. Production orchestration requires: retry logic with exponential backoff for transient failures, fallback paths when a primary model fails, circuit breakers to stop cascading failures, dead letter queues for failed workflow runs that need human review, and alerting when failure rates exceed thresholds. We design failure handling as part of the orchestration architecture, not as an afterthought.
Multi-step AI workflows accumulate context that can exceed model context windows. Management strategies: summarisation (compress earlier workflow steps into summaries), selective context (include only the most relevant prior steps based on the current task), external memory (store workflow state in a database rather than the context window), and context chunking (process large inputs in segments). The right strategy depends on your workflow structure and the information dependencies between steps.
A framework earns its place when it solves a non-trivial problem: state persistence, loops, retries, tool coordination. For a single-turn agent, frameworks add latency, update churn, and debugging overhead a short custom loop avoids. The test is whether you are fighting the framework: if its abstractions do not match your control flow, it costs more time than it saves. Start managed for the MVP, migrate to custom for production: custom gives you full audit trails and control over where data flows.
Track cost per completed outcome, not per model call. The cheapest model is not the cheapest workflow if it retries twice or needs more validation. Demand token-aware budgets per tenant, semantic caching, smart model routing, and prompt compression. And set a per-task ceiling so one runaway loop cannot burn a week's inference budget in an afternoon.
Ask what happens when a step fails mid-workflow: can it retry safely, resume from a checkpoint, or route to a human? Look for an observability layer that shows every step, its cost, and its outcome, not a black box. Ask how the vendor prevents a runaway loop from becoming a runaway bill. And test with your hardest real documents: the demo works, the first real document is where orchestration earns its keep.
Pricing is set after we scope your workflow, integration points, and failure-handling requirements. You get a fixed price in writing before development starts, and a scope change is a priced change request you approve first. Most builds start with a single workflow before routing expands to the rest.