Service - LLM application development
Build LLM-powered product features that are reliable in production, grounded in your business context, and maintainable by your team.
Who this is for
Teams building real LLM product value, not demo features
- SaaS founders adding chat, copilots, or automation into existing products
- Teams building AI workflow products with reliability requirements
- Startups replacing fragile demo logic with production-ready systems
Production priorities
- Latency and cost controls
- Context quality and retrieval precision
- Observability for model behavior
Problems
Problems we solve
- LLM features that work in demos but fail under real user conditions
- Weak context and memory design that reduces output quality
- No observability around prompts, retrieval quality, and response behavior
Execution
How we work
Model and architecture selection based on cost, latency, and control
Iterative implementation with failure-mode testing
Launch support with monitoring hooks and rapid quality loops
Capabilities
Relevant technical capabilities
Orchestration
Routing, fallback logic, and tool-invocation control
RAG pipelines
Retrieval flows tuned for relevance and response quality
Context windows
Context shaping that keeps answers grounded and useful
Memory layers
Session and state handling that supports repeat interactions
Tool calling
Reliable action execution across external systems
Backend APIs
Production APIs built for versioning and release safety
Chat UX
Conversation interfaces designed for clarity and trust
Observability
Tracing and evaluation hooks for continuous improvement
Engagements
Example engagements
Customer-facing AI chat integrated into a B2B SaaS product with production-safe retrieval workflows and maintainable release ownership.
Internal operations assistant built on private knowledge retrieval so teams can reduce repeated manual lookups and keep decisions traceable.
Workflow automation system designed to reduce operator dependency and move fragile manual processes into production software.
Partnership
Why Software Chains
We prioritize usable systems over AI theater. You get clear technical ownership, practical architecture decisions, and direct communication through delivery.
FAQ
Questions founders ask before starting
Do you work with OpenAI, Claude, Bedrock, LangGraph, or custom AI workflows?
Yes. We design against your product constraints and can implement with OpenAI, Claude, Bedrock, and custom orchestration patterns.
Can you improve an existing SaaS product?
Yes. We can integrate LLM features into existing products while improving architecture and release safety.
Can you build an AI MVP from scratch?
Yes. We can deliver the first end-to-end version with chat, retrieval, automation, and core product flows.
Related pages: AI product development for startups • Fractional CTO + product engineering • Homepage • Process • See anonymized LLM product work • Contact