Customize AI Tool Development

We build bespoke AI tools across domains — document processors (contracts, invoices, compliance filings), legal and regulatory AI assistants, customer support chatbots, sales and marketing copilots, data extraction pipelines, voice AI, and multi-agent workflow automations. Our team has production experience with OpenAI GPT-4, Anthropic Claude, Google Gemini, open-source Llama, and vector databases like Pinecone and pgvector.

Yes. HOA Fighter (myhoafighter.com) is a production AI tool our team designed and built. It helps US homeowners analyze HOA violation letters across 8 procedural checkpoints — notice period, hearing offered, fine schedule, CC&Rs, state law, selective enforcement, cure period, and statutory caps — and drafts a response letter for them to review with their attorney. HOA Fighter provides general legal information only, not legal advice, and does not create an attorney-client relationship. It is a live, real-world example of a domain-specific AI tool combining document AI, LLM reasoning, structured prompt engineering, and a focused user experience.

A functional MVP typically takes 3-6 weeks depending on scope. Week 1 is discovery and prompt design. Weeks 2-3 cover LLM integration, vector-store setup if RAG is required, and backend APIs. Weeks 4-5 are UI build and quality testing. Week 6 is hardening, prompt caching setup for cost reduction, and deployment. Enterprise-grade tools with fine-tuning, compliance review, or multi-tenant architecture add 4-8 weeks.

We integrate with all major providers — Anthropic Claude (our default for reasoning-heavy tools), OpenAI GPT-4 and GPT-4o, Google Gemini, Azure OpenAI, AWS Bedrock, and open-source models (Llama 3, Mistral, Qwen) via Together AI, Groq, or self-hosted vLLM. We help you pick the right model for your use case based on latency, cost, context window, and output quality.

We design AI tools with privacy-first architecture. Sensitive data is processed via providers with zero data-retention agreements (Anthropic, enterprise OpenAI tiers). For regulated workflows we support on-premise LLM deployment, PII redaction pipelines, full audit logging, and compliance with GDPR, HIPAA, SOC 2, and Indian DPDP requirements. We also set up prompt caching to reduce costs while keeping data isolation guarantees.

Yes. AI tools require continuous tuning as models upgrade and user patterns evolve. We offer monthly retainers that include prompt iteration, eval-set maintenance, LLM version migrations (e.g. Claude 4.5 to 4.6 to 4.7), cost optimization via prompt caching, latency tuning, and new feature development. Most clients run our team as an extended AI engineering pod.