Prompt & Orchestration Engineering

Design reliable, multi-step LLM workflows that perform consistently at scale in production.

LLM Orchestration Patterns We Use

Different tasks need different orchestration patterns. We select and implement the right one for your use case.

Chain of Thought
Break complex reasoning into sequential steps. Each step builds on the previous output.
Best for: Complex analysis, multi-step reasoning
ReAct Agent
Reason → Act → Observe loop. The model uses tools and adapts based on results.
Best for: Research tasks, data retrieval, calculations
Multi-Agent
Specialized agents collaborate — planner, executor, critic — each with a defined role.
Best for: Complex workflows, quality assurance
Structured Output
Force the model to produce JSON, tables, or typed schemas your systems can consume.
Best for: Data extraction, API integration

What we build

Prompt engineering is not just about writing good instructions — it is about designing systems that produce reliable, consistent outputs across thousands of real-world inputs. We build prompt architectures and orchestration pipelines that are tested, versioned, and optimized for production performance.

For complex tasks, we design multi-agent and chain-of-thought workflows that break problems into manageable steps, use tools and external data sources, and produce structured outputs your systems can act on.

Key deliverables

  • Prompt design, testing, and systematic versioning
  • Few-shot and chain-of-thought prompt strategies
  • Multi-agent orchestration with LangChain, LlamaIndex, or custom frameworks
  • Tool use and function-calling integration
  • Structured output design and validation
  • Prompt regression testing and performance benchmarking
40%
Output quality improvement from systematic prompting
60%
Cost reduction with optimized prompt design
100%
Prompt changes tested before deployment
3×
Faster iteration with versioned prompt systems

Real-Life Use Cases

Prompt and orchestration engineering delivering reliable LLM systems.

Insurance

Multi-Step Claims Analysis

An insurer needed to analyze claims documents, extract key facts, assess coverage, and generate a recommendation. We designed a 4-step chain-of-thought pipeline with structured outputs at each stage. Accuracy improved from 67% to 91% vs a single-prompt approach.

Accuracy: 67% → 91% with chain-of-thought
Finance

Research Agent with Tool Use

A financial research team needed an AI agent that could search filings, pull financial data, and synthesize investment theses. We built a ReAct agent with SEC filing search, financial data API, and calculator tools. Research time per company dropped from 4 hours to 25 minutes.

4 hours → 25 minutes per company research
Software

Code Review Orchestration

A software team built a multi-agent code review system: a security agent, a performance agent, and a style agent each review independently, then a synthesis agent produces a unified report. PR review quality improved significantly and review time dropped 70%.

70% reduction in code review time
Marketing

Content Generation Pipeline

A marketing team's single-prompt content generation produced inconsistent results. We redesigned it as a structured pipeline: research → outline → draft → edit → format. Output quality scores from human reviewers improved from 3.1 to 4.4/5.

Content quality: 3.1 → 4.4/5 human rating

Build LLM workflows that work reliably at scale

We'll design the prompt architecture and orchestration pipeline that delivers consistent, production-quality outputs.

Engineer Your LLM Workflows