AI orchestration that holds up in production, not just the demo.

• 01

  Multi-step document workflows
  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. Most document workflows we build process 90-95% of volume automatically and route 5-10% to review. Typical stack: Claude Haiku and GPT-4o mini.
• 02

  AI agent systems
  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 destructive actions, and escalation to a human when state is unrecoverable. Every tool call is logged, and supervisor-worker architectures parallelise tasks that benefit from it.
• 03

  Multi-model pipelines
  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 cuts total inference cost by 40-70%, 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.
• 04

  RAG with re-ranking
  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. Re-rankers typically improve answer accuracy by 15-25% over naive retrieval on domain-specific corpora.
• 05

  Human-in-the-loop workflows
  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 action is logged, producing the audit trail compliance teams require. The AI handles volume; humans handle judgment.
• 06

  Production monitoring and observability
  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.

1. Week 1
   01

   Discover and map

   We map the workflow, the models, the tools, and the failure modes. You leave week 1 with a written orchestration spec and a fixed-price quote. No development starts without your sign-off.

2. Weeks 2-3
   02

   Design the architecture

   We design the graph, the routing logic, the state schema, and the human-in-the-loop points before writing production code. Decisions made here cost ten times less than the same decisions made in week 8.

3. Weeks 4-12
   03

   Build, integrate, and QA

   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.

4. Weeks 12+
   04

   Deploy and monitor

   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.

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