AI Orchestration Services | Multi-Model Pipelines

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 that task -- a fast classifier (Claude Haiku or GPT-4o mini) for document type routing, a precise extraction model for pulling specific fields from variable layouts, and a reasoning model for edge cases that require interpretation. Step outputs are validated against business rules before passing to the next step -- a PO number that doesn't match any open purchase order is flagged for review, not silently written to the ERP. Exceptions surface to a human review queue with full context: the document, the extracted fields, and the specific validation failure. Most document workflows we build process 90-95% of volume automatically and route 5-10% to review.

Stateful AI agents that plan and execute multi-step tasks using tools: database queries, external API calls, code execution, file operations, and web search. Built with LangGraph for reliable state management across a workflow that might span 5-20 tool calls with branching logic depending on what each step returns. LangGraph's checkpointing persists agent state after every step so an agent interrupted by a timeout or error can resume from the last checkpoint rather than restarting from the beginning -- critical for long-running agentic workflows.

The agent receives a goal, reasons about which steps are required, calls tools in the right sequence, and adjusts its plan when a tool returns an unexpected result or fails. Guardrails prevent the failure modes that make agents unreliable in production: maximum step counts that halt agents caught in retry loops (bounded planning), permission scoping that ensures the agent can only call the tools it has been authorised to use for this task, confirmation requirements before destructive or external-communication actions (sending an email, updating a database record), and unrecoverable state detection that surfaces an escalation to the human operator rather than silently failing. Every tool call is logged with inputs, outputs, and latency for debugging and compliance audit. Multi-agent architectures (supervisor + worker agents) for tasks that benefit from parallelisation: a research agent that dispatches 5 simultaneous sub-agents each investigating a different source and synthesises their outputs is faster than a single agent investigating serially.

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 tasks (intent detection, document type routing, sentiment scoring) go to Claude Haiku or GPT-4o mini at $0.15-0.60/million tokens. Complex reasoning, synthesis, and generation tasks go to Claude Sonnet or GPT-4o at $3-15/million tokens. Structured extraction from known schemas uses function calling with a mid-tier model. Code generation and analysis uses a specialist code model. Smart routing reduces total inference cost by 40-70% compared to routing everything to the top-tier model, with accuracy trade-offs measured against your evaluation dataset, not assumed. Model fallback logic switches to a backup provider when primary model latency exceeds your SLA threshold.

Retrieval-augmented generation pipelines engineered for production accuracy, not benchmark performance. The common failure mode in RAG is that initial retrieval (top-k vector search) returns the k most semantically similar chunks, but similarity is not the same as relevance to the specific question. We build retrieval pipelines that over-retrieve (top 20-50 candidates), then re-rank using a cross-encoder model or LLM judge that scores each candidate against the query for actual relevance before the top 3-5 are included in context. Hybrid retrieval combines semantic vector search (Pinecone, Weaviate, or pgvector) with BM25 keyword search to catch exact-match terms that vector search misses. Query expansion reformulates ambiguous queries into multiple search variants (a user asking "how do I cancel?" might be better answered by retrieving documents tagged with "cancellation", "termination", and "unsubscribe"). Contextual compression using LLM extraction strips irrelevant sentences from retrieved chunks before they enter the context window -- improving answer quality and reducing token cost. Re-rankers typically improve answer accuracy by 15-25% over naive top-k retrieval on domain-specific corpora.

AI workflows with explicitly designed human intervention points -- because full automation is not always the right architecture, especially in regulated industries or high-stakes decisions. Low-confidence model outputs (below a configurable threshold) route to a human review queue with the document, the model's output, and its confidence score displayed side-by-side for efficient review. High-stakes decision categories -- contract approvals, medical flags, financial exceptions -- require human sign-off before the workflow continues, with a time-boxed SLA and escalation if the review stalls. Exception cases are routed to specialist queues based on category, not dumped into a generic inbox. Every AI decision and human review action is logged with timestamp, actor, and the specific output reviewed -- producing the audit trail that compliance and legal teams require. The AI handles the volume; humans handle the cases that require judgment.

Full observability across every orchestration step, implemented via LangSmith, Langfuse, or a custom telemetry layer: 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 (completed workflows per hour), success rate (workflows that completed without human intervention), average cost per run, and step-level latency percentiles. Alerting fires when error rates spike, when average latency exceeds your SLA, or when cost per run increases 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 defined schedule to detect accuracy regressions from model updates or prompt drift before they reach production.