Custom GPT-style AI built on your data and workflows.
We integrate large language models — OpenAI, Claude, Gemini, and open-source LLMs — into your products with RAG, fine-tuning, and secure enterprise deployment.
Production applications built on GPT-4o, Claude, Gemini, and Llama with structured outputs, function calling, and guardrails. We design for reliability first — evals, fallbacks, and cost controls are part of every build.
We connect LLMs to your documents, wikis, and databases using vector search (Pinecone, Weaviate, pgvector) so answers are grounded in your data. Properly tuned RAG cuts hallucination dramatically and keeps proprietary knowledge in-house.
LoRA and full fine-tuning of open models like Llama and Mistral on your domain data, plus OpenAI fine-tuning when managed APIs fit better. Fine-tuned smaller models often match larger ones on narrow tasks at 10x lower inference cost.
Automated drafting of reports, product descriptions, contracts, and marketing copy with human-in-the-loop review workflows. Clients cut first-draft time by 60-80% while editors keep final control.
We embed generative AI into the tools you already use — Salesforce, SAP, Microsoft 365, Slack — through secure APIs and middleware. Data governance, role-based access, and audit logging come standard for compliance teams.
A 2-4 week sprint that identifies your highest-ROI generative AI use cases and ships a working prototype against real data. You get measurable results and a costed roadmap before committing to a full build.
We pick the right technology for your goals — not the trendiest framework.
Terms that go into every agreement we sign. See all client commitments.
Every job gets a scope and a number in writing first — a fixed price for defined work, or an hourly rate with a capped estimate. If scope changes, we re-quote before we build, not after.
Source code, designs, and data are yours. We work in your repository where possible, hand over full documentation, and transfer everything at the end. No license fees, no hostage situations.
We sign your NDA — or send ours — before the first technical conversation. Your idea, your data, and your customer information stay confidential during and after the engagement.
Everything you need to know before starting your project. Still have questions? Talk to our team.
Internal knowledge assistants, document automation, customer-facing copilots, code generation tools, and content pipelines — built on OpenAI, Anthropic, Google, or self-hosted open-source models. We choose the model per use case based on quality, latency, privacy, and cost.
A proof of concept takes 2-4 weeks; a production-ready RAG assistant or copilot takes 2-4 months including evaluation, security review, and integration. Fine-tuning projects add 3-6 weeks for data preparation and training cycles.
Development typically runs $15,000-$60,000 depending on integrations and data complexity. Ongoing LLM API costs are usage-based — most mid-size deployments spend $200-$2,000/month, and we routinely cut that 40-60% with caching, prompt optimization, and routing cheaper models to simple queries.
You own all application code, prompts, and fine-tuned model weights we create. Your data is never used to train public models — we use zero-retention API agreements or deploy open-source models inside your own cloud (VPC) for full GDPR and SOC 2 alignment.
We ground responses in your verified data via RAG, add output validation and guardrail layers, and build automated eval suites that score accuracy before every release. Production systems include confidence thresholds and human-escalation paths for low-certainty answers.
We deploy to your cloud (AWS, Azure, GCP) with monitoring on latency, cost, and answer quality, plus 90 days of included post-launch support. Monthly AI ops retainers cover model upgrades — providers ship better, cheaper models every few months, and we keep you on the best one.
Get a free consultation and a detailed project estimate within 24 hours.
