Pillar · Generative AI
Generative AI for Enterprise — sovereign, regulated, in production
MindMap Digital ships generative AI solutions to 50+ Fortune-class customers across BFSI, healthcare and government. Sovereign deployment by default. Open-weights LLMs (Llama 3.3, Qwen 2.5, Mistral, DeepSeek) on customer infrastructure. Six application categories from sovereign LLM platforms to agentic workflows. First pilot to production in 6–9 weeks.
SovereignOpen-weightsLlama 3.3Qwen 2.5MistralDeepSeek
Definition
Generative AI, defined for the enterprise.
Generative AI is the class of AI systems that produce new content — text, images, code, structured data — by learning patterns from training data. In enterprise practice, generative AI almost always means Large Language Model (LLM) applications: chatbots, document understanding, content generation, agentic workflows. The key engineering question for enterprise GenAI isn't “can the model do it” — by 2026 it usually can — but “can we deploy it in a way that satisfies the regulator, integrates with our systems, and produces measurable ROI.”
Six application categories
What enterprise generative AI actually deploys as
Six categories that account for 90%+ of MindMap's 50+ GenAI deployments. Most enterprises start with one and expand to others on the same sovereign platform.
Sovereign LLM platform
Open-weights LLM (Llama 3.3, Qwen 2.5, Mistral) deployed on customer infrastructure with full inference, observability, evaluation and audit substrate. The platform every other GenAI use case runs on.
11 days clean cluster to first prompt; 60+ tokens/sec on a single A100
Retrieval-Augmented Generation (RAG)
Production RAG over the customer's policy/knowledge corpus. Hybrid retrieval (dense + BM25 + re-ranker), semantic chunking, eval harness with SME-written question bank.
80%+ context precision; 89% answer-correct rate on tier-1 corpora
Agentic workflows
LLM-driven multi-step agents for judgment-based tasks — prior auth review, claims first-notice-of-loss triage, compliance Q&A, fraud-alert triage. Bounded autonomy with full audit trail.
71% auto-decision rate on prior auth; 5d → 4h cycle compression
Conversational AI / chatbots
Customer-facing chatbots, voice agents, WhatsApp banking. ChatNext sustained at 65-70% deflection on tier-1 query categories. 12 languages.
65-70% sustained deflection; 50-70% agent reallocation value
Document Intelligence
LLM-augmented document extraction for unstructured/long-tail document types. Schema-driven extraction with confidence-scored field-level review.
94% straight-through processing across heterogeneous document types
Internal knowledge assistants
Employee-facing GenAI grounded on the company's internal knowledge base — HR policies, IT runbooks, sales product knowledge, compliance procedures. Sovereign deployment, audit-trail by default.
60-75% deflection on internal queries; substantial productivity uplift
Model selection
Open-weights LLMs for sovereign deployment
Six open-weights families ship into our deployments. The capability gap is now in single digits on enterprise workloads — the decision is licensing, language, and operational fit.
Llama 3.3 (Meta)
Llama 3 Community Licence (permissive)
General enterprise workloads; strongest English benchmark; broad ecosystem support
31 of our 50+ deployments
Qwen 2.5 (Alibaba)
Apache 2.0 (most variants)
Multilingual workloads (Chinese, Arabic, Hindi, Bengali, South-Asian languages)
9 deployments, Gulf + APAC concentration
Mistral Large 2
Mistral Commercial Licence
Structured-output tasks; tool-use; function-calling; agentic workflows
5 deployments
DeepSeek V3
MIT
Reasoning-heavy agentic workloads; strongest per-parameter reasoning
3 deployments and growing fast
Phi-3.5 (Microsoft)
MIT
Edge / small-footprint deployments; low-latency real-time use cases
1 deployment + edge prototypes
Gemma 3 (Google)
Apache 2.0
Google-ecosystem integration; similar capability to Llama 8B class
1 deployment
Sub-pillars
Specialist generative AI practices
Sovereign AI
On-prem, air-gapped LLM deployment for regulated industries
Open →
Enterprise RAG
Retrieval-augmented generation over your knowledge corpus
Open →
Agentic AI
LLM-driven multi-step workflows with bounded autonomy
Open →
Conversational AI
Chatbots, voice agents, WhatsApp banking — 65-70% deflection
Open →
Document Intelligence
LLM-augmented IDP — 94% straight-through processing
Open →
EU AI Act
Articles 9-15 compliance for high-risk GenAI systems
Open →
FAQ
Generative AI — the questions buyers ask
What is generative AI?
Generative AI refers to AI systems that can produce new content — text, images, code, structured data — by learning patterns from training data. In enterprise deployment, generative AI most commonly means Large Language Model (LLM) applications: chatbots, document understanding, content generation, agentic workflows. The key engineering question for enterprise GenAI isn't 'can the model do it' — by 2026 it usually can — but 'can we deploy it in a way that satisfies the regulator, integrates with our systems, and produces measurable ROI.'
What are generative AI solutions for enterprise?
Six main categories: (1) sovereign LLM platforms — the underlying infrastructure every other use case runs on; (2) RAG (retrieval-augmented generation) over company knowledge; (3) agentic workflows for judgment-based decisions; (4) conversational AI — chatbots and voice agents; (5) document intelligence — LLM-augmented IDP; (6) internal knowledge assistants for employees. Most enterprises start with one and expand to others on the same sovereign platform.
What is a generative AI company?
MindMap Digital is an enterprise generative AI company — we design, build and deploy generative AI applications for regulated enterprises that can't send data to public cloud LLM APIs. Different from frontier-model companies (OpenAI, Anthropic, Google) who train foundation models, we deploy open-weights LLMs (Llama, Qwen, Mistral, DeepSeek) on customer infrastructure, integrate them with the customer's estate, build the audit substrate, and operationalise the result.
How do we choose between Llama, Qwen, Mistral and DeepSeek?
Llama 3.3 70B is the default for English-primary enterprise workloads (31 of our 50+ deployments). Qwen 2.5 for multilingual workloads where Chinese, Arabic or South Asian languages matter. Mistral Large 2 for structured-output and tool-use heavy workloads. DeepSeek V3 for reasoning-heavy agentic workloads. The capability gap across these families is now in the single-digit percentage points; the decision is licensing-driven, language-driven, or driven by the customer's integration substrate.
Should we use generative AI from the cloud or on-premise?
For regulated industries (BFSI, healthcare, government, defence), sovereign on-premise deployment is the default architecture — model weights on customer infrastructure, inference logs in customer SIEM, the entire stack operable air-gapped. For non-regulated workloads with low token volume, cloud APIs are still simpler. The economic cross-over is around 200M tokens / month — above that, on-prem is materially cheaper. Above 5B tokens / month, the cost calculus is no longer the decision driver; the regulatory posture is.
What generative AI consulting services do you offer?
Generative AI Strategy Consulting (use-case prioritisation, business case, ROI modelling). Generative AI Readiness Sprint (2-week diagnostic). First Pilot (6-9 weeks contract-to-production sovereign deployment). Generative AI Transformation (multi-quarter Centre of Excellence build). Generative AI Compliance (Articles 9-15 evidence for high-risk EU AI Act systems). Generative AI Managed Services (long-term operational responsibility for the platform).
What's the ROI on generative AI for enterprise?
Depends heavily on the use case. Conversational AI: €1-4M annual contact-centre cost reduction at typical mid-market BFSI volumes. Document Intelligence: €0.8-2.5M annual savings on doc-heavy workflows. Agentic workflows for prior auth / claims: $14 labour saved per case at clinical-reviewer rates. Internal knowledge assistants: 12-18% productivity uplift on knowledge-worker time. Most well-scoped engagements deliver 3-9 month payback against €180-340k First Pilot cost.
Ready to ship enterprise generative AI?
Sovereign deployment. Open-weights. 6–9 weeks. 117-accelerator library means we don't start from zero.