AI rollouts at scale, from POC to production with governance, multi-LLM and architectures that respect your operation.
Multi-LLM
Anthropic Claude · OpenAI · Google Gemini · Llama and Mistral with local Ollama
End-to-end
from POC to production with evaluation, observability and cost tracking included
RAG production-ready
vector DBs + retrieval + automatic evaluation + citation tracking
Technical neutrality
no partnerships with AI vendors, we recommend based on the case's cost-precision
Models and frameworks we build with
01. Context
80% of enterprise AI projects never reach production according to the latest reports from Gartner and McKinsey. The reason is rarely technical: it is operational. Models are trained, prompts are written and workflows work in demo, but when the case collides with raw uncurated data, with poorly designed granular permissions, with inference costs that skyrocket when scaling, with outputs that need human validation in regulated markets, or with a team that does not know how to maintain the system when the consultants leave, the project stays in eternal pilot. Consultancies that close that gap between POC and production are the ones that truly move the needle.
We work with all the leading models. Anthropic Claude for deep reasoning and long contexts, OpenAI GPT and o-series for generalist cases, Google Gemini for multimodal at competitive cost, open source models with local Ollama (Llama, Mistral, DeepSeek) for cases where the data cannot leave your infrastructure, and we orchestrate everything with production-grade frameworks (LangChain, LlamaIndex, custom). The architectures we deliver cover four pillars: RAG on proprietary data with vector databases (Pinecone, Weaviate, Qdrant); agentic AI Agents with persistent memory and tool calling to automate complete flows, not just answer questions; document processing pipelines for invoices, contracts and back-office; and native integration with the CRM, knowledge base and operational tools you already use daily.
02. How we apply it
The applications that come up most often in our projects.
RAG on proprietary data
Assistants that answer with precision over the client's internal documentation: support knowledge bases, research archives, contracts, technical manuals. Vector databases + retrieval + automatic evaluation + citation tracking so the end user knows where each answer comes from. It is the flagship enterprise AI use case.
Agentic AI Agents in production
Agents that execute multistep tasks with persistent memory, tool calling and human-in-the-loop on critical steps. Customer support, sales prospecting, deep research, account briefings. Designed for environments where reliability matters more than novelty: each decision traceable, each output evaluated.
Document processing and classification
AI pipelines to process invoices, contracts, claims, case files and forms. OCR + structured extraction + classification + routing + human validation in sensitive cases. Changes the unit economics of back-office teams, what previously required 5 people goes to requiring 1 who supervises.
Content automation at scale
Generation of editorial, marketing and client communication content: with native human review, persistent brand voice, multichannel publishing and observability on which content generates engagement. Not content chatbots; editorial production systems.
Sales acceleration with AI
Automated account research, pre-call briefings with enriched context, outreach drafts personalized by segment, intent scoring and automatic qualification. Integrated with the CRM so every AI action is recorded and attributable to the pipeline.
03. What changes for you
At The Optimal Flow we operate AI as one of the consultancy's core specializations. We take systems to production, not demos to steering committees, and that difference defines how we approach each project. Most AI implementations die because someone left a prompt running in a notebook, a silent model on an endpoint without observability, or an agent responding without traceability of which data it used. We design from day one with automatic evaluation (which prompt performs best in which case), full observability (what was asked, what the model answered, what happened next), granular cost tracking (what each query costs, where to lower cost without losing quality) and a clear upgrade path for when the next model that changes the cost-precision calculation appears.
Where we extract the most value from your investment is in technical neutrality between vendors. We have no formal partnerships with OpenAI or Anthropic or Google, and that means we recommend the model based on the case, not on revenue share. Claude for complex reasoning and long contexts, GPT for generalist cases with a mature tooling ecosystem, Gemini when cost per token matters more than marginal quality, local models with Ollama when the data cannot leave your infrastructure. And we design the system so the model can be swapped without rewriting the application, because in this market the SOTA changes every six months. The AI we build for you integrates with the rest of your operation: with your CRM, your knowledge base, your automated flows, not as an island with a chatbot on top, but as one more layer of the system your team already operates.
Book a free intro session so we can understand where you stand and how we can help. No strings attached.
or write to us at info@theoptimalflow.com
