We help mid-market and enterprise teams move from AI pilots to production — strategy, data foundations, generative AI, agentic workflows, MLOps, and managed services under one roof.
> rag eval --suite=golden-set --top-k=8 --rerank=cohere • Loading 412 golden examples • Hybrid retrieval ready (BM25 + dense + rerank) Groundedness ........... 0.94 (target ≥ 0.90) PASS Context precision ....... 0.88 (target ≥ 0.80) PASS Context recall .......... 0.81 (target ≥ 0.75) PASS Answer faithfulness .... 0.92 (target ≥ 0.90) PASS Hallucination rate ..... 0.6% (target ≤ 2.0%) PASS All gates passed. Promoting to canary.
Production checklist
Delivered in
8 weeks
From kickoff to canary rollout.
We choose the right tool for the workload, the regulator, and the budget — and design every system to remain portable.
Cloud
Foundation models
Data & lakehouse
Vector & retrieval
Orchestration & ops
Eval, guardrails & observability
We pick the model and framework for the workload, the regulator, and the budget — never the marketing slide. Below: what we ship with each.
Anthropic Claude
Foundation Model
Long-context reasoning, careful tool-use behaviour, and safety posture suited to regulated deployments.
OpenAI
Foundation Model
Broadest ecosystem and tooling for general-purpose copilots, code generation, and multimodal applications.
Google Gemini
Foundation Model
Multimodal models with very large context windows, integrated through Vertex AI with BigQuery and Workspace.
Meta Llama
Foundation Model
Open-weights models for self-hosted, sovereign, or cost-sensitive deployments where vendor portability matters.
Mistral
Foundation Model
European-sovereign foundation models with strong open and licensed options for GDPR-sensitive workloads.
Cohere
Foundation Model
Enterprise embeddings and rerank models that materially improve retrieval quality in production RAG.
LangChain
Framework
Application framework we use when an LLM project has many integrations, complex prompt chains, or evolving tools.
LangGraph
Framework
Stateful agent orchestration with explicit graphs, memory, and human-in-the-loop checkpoints. Our default for serious agentic systems.
LlamaIndex
Framework
Data framework for LLMs — strong choice when RAG quality depends on advanced indexing, structured extraction, or knowledge graphs.
Hugging Face
Framework
Model hub, training, and fine-tuning infrastructure. The default when we need open-source models or a managed fine-tuning pipeline.
Most consultancies cover three of these. We run all twelve under one delivery model so your roadmap doesn’t shatter every time a new vendor enters the room.
AI buyers want pattern-matched, regulated-friendly use cases — not lab demos. Every engagement starts with a vertical-specific catalogue of what has worked elsewhere, calibrated to your data and your constraints.
A 2-week sprint to map your data, processes, and the use case with the highest ROI — with named KPIs and a build plan you can fund.
A 6–12 week build that ships behind real eval gates — not a demo. Includes guardrails, observability, and a rollback plan from day one.
Roll out to a controlled cohort, measure the production KPI, then scale. We do not promote to 100% until the metric proves out.
Drift detection, scheduled retraining, incident response, and quarterly red-team campaigns — under a single SLA.
We measure success in production metrics — accuracy, latency, cost, adoption — not deck pages. Every engagement names the KPI on day one.
Models drift, data changes, regulations land. Our managed-service model keeps the system improving long after launch — not just shipping once and walking away.
ISO 42001 alignment, EU AI Act readiness, ISO 27001 controls, and red-teaming are part of the standard delivery — not a paid add-on.
12+
AI service lines under one roof
6–12 wk
Typical MVP-to-production timeline
24/7
Managed-service incident response
ISO 42001
AI governance readiness built in
The questions that come up most often in our first conversations. Short, honest answers.
PCCVDI is an AI implementation consultancy. We help mid-market and enterprise teams move AI from pilot to production across twelve service lines — strategy, data foundations, training data, generative AI, agentic workflows, NLP, computer vision, classical ML, MLOps, governance, managed services, and enablement — under one accountable delivery model.
Book a 30-minute consultation. We will walk through the use case, sketch the value case, and tell you honestly whether we can help.
