From POC to Production in 12 Weeks

12 weeks is our standard for single-system deployments (one LLM app, one RAG system, one agent). Complex multi-system projects take 16–20 weeks. You see working demos from week 2 — not a big reveal at the end.

Single-system projects (LLM app, RAG, or agent) range from $75K–$150K. Multi-system enterprise deployments range from $150K–$400K+. Every engagement starts with a 2-week paid discovery ($15K–$25K) that produces a working prototype — so you see results before committing to the full build.

Agentic AI systems autonomously execute multi-step tasks — research, decide, act, verify — with minimal human oversight. You need it if you have workflows that follow predictable logic but require judgment calls: claims processing, document review, code generation, customer escalation. Gartner predicts 40% of enterprise apps will feature AI agents by end of 2026.

It depends on your requirements. OpenAI GPT-5 excels at general tasks and function calling. Anthropic Claude is strongest for long documents and careful reasoning. Open-source (Llama, Mistral) is best when data must stay on-premise or you need fine-tuned domain performance. We evaluate all options against your cost, latency, privacy, and accuracy requirements.

RAG (Retrieval-Augmented Generation) grounds LLM responses in your actual data — documents, databases, knowledge bases — instead of relying on the model's training data alone. It eliminates hallucinations and keeps answers current. We build RAG systems that achieve 80–90% answer accuracy vs 40–50% with standard keyword search.

Multiple layers: input validation, output filtering, guardrail policies, human-in-the-loop for high-stakes decisions, and continuous monitoring for drift and adversarial inputs. For regulated industries, we add model explainability (SHAP/LIME), bias testing across demographics, and immutable audit trails.

Yes — we integrate with Salesforce, SAP, Oracle, ServiceNow, Jira, Slack, Teams, SharePoint, Confluence, custom APIs, and most enterprise platforms. Integration architecture is designed in week 1 so there are no surprises later.

Every project includes 30 days of post-deployment support. For ongoing operations, our Managed AI Services team provides 24/7 monitoring, automated retraining, and continuous optimization. Most clients transition to managed services — it's cheaper than building an internal MLOps team.

Yes — knowledge transfer is built into every sprint. Your engineers pair with ours during development. We deliver comprehensive documentation, recorded walkthroughs, and a 2-week enablement period. The goal is to make your team self-sufficient for day-to-day operations.

40% of our clients come to us after a failed first attempt. Common failure causes: insufficient data preparation, wrong model for the use case, no production architecture from the start, or scope creep. We audit what went wrong, salvage viable components, and rebuild on a proven architecture.