AI Software Development — Not Demos, Production
AI features and autonomous agents built to run reliably in the real world — with error handling, fallbacks, and monitoring from day one.
Types of AI work we ship
Across every type, the approach is the same — evaluation-driven, production-ready, and built to fail gracefully.
Autonomous AI Agents
Multi-step agents that research, decide, and act — with tool use, memory, and human-in-the-loop review steps where accuracy is critical.
LLM Integration
Structured integration of OpenAI, Anthropic, and open-source models into your existing product — with proper error handling, cost controls, and fallback logic.
Document Intelligence
Extract, classify, and reason over unstructured documents — contracts, invoices, reports — at scale, with human-review workflows for edge cases.
RAG Systems
Retrieval-augmented generation that actually works — proper chunking, embedding strategy, retrieval ranking, and response quality evaluation.
Workflow Automation
Replace manual, repetitive processes with AI-driven pipelines — customer support triage, data enrichment, report generation, and more.
AI Infrastructure
Vector databases, embedding pipelines, model serving, and monitoring setups that keep your AI features running reliably in production.
The gap between a demo and production AI
What most teams ship first
- A prompt that works 80% of the time
- No handling for API rate limits or failures
- No cost controls or usage monitoring
- Hallucinations caught by users, not the system
- No evaluation framework for output quality
What HDTL builds
- Structured outputs with validation and retry logic
- Graceful fallbacks when models are unavailable
- Token budget enforcement and cost dashboards
- Confidence thresholds and human-review queues
- Automated evaluation pipelines that track quality over time
Frequently asked questions.
Can you add AI features to our existing software?
Yes. We regularly integrate AI into systems we did not originally build — adding natural-language search, document extraction, agent automation, or AI-powered recommendations as layered features on top of existing infrastructure.
How do you handle AI hallucinations in production?
Through structured outputs, strict prompt engineering, confidence thresholds, human-in-the-loop review steps where accuracy is critical, and monitoring dashboards that flag anomalous outputs. We design AI systems for the failure cases, not just the happy path.
What LLM providers do you work with?
Primarily OpenAI, Anthropic (Claude), and open-source models via Ollama or Hugging Face. We help you choose the right model for your use case based on cost, latency, capability, and data-privacy requirements.
How do you handle sensitive data in AI systems?
We design data pipelines with privacy in mind — anonymizing where possible, restricting what gets sent to external APIs, and offering on-premises or private cloud options when data residency is a hard requirement.
How long does it take to ship an AI feature?
A focused AI integration — such as adding document extraction or an AI search layer — typically ships in 4 to 8 weeks. A full autonomous agent system is 10 to 16 weeks depending on complexity and the number of external tools it needs to access.
Let’s build something that lasts.
Tell us what you’re building. We’ll tell you exactly how we’d build it.
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