Generative AI Consulting for Enterprises That Need It Private, Governed, and Grounded in Their Own Data
MinervaDB's generative AI consulting practice takes enterprises from use-case portfolio to production: retrieval-augmented generation and agentic workflows that run inside your VPC, honour row-level entitlements at retrieval time, log every prompt and answer for audit, and are evaluated continuously for groundedness, cost, and policy compliance. Built on the vector data engineering, governance, and database-grade operations that our clients already rely on.
Generative AI Consulting Built on Database-Grade Engineering
A demo that answers questions about public documents takes an afternoon. A system that answers questions about your contracts, for the people entitled to see them, with evidence, at a known cost, is engineering. We do the second kind.
Generative AI at MinervaDB is treated as a data system with a language model attached, not the other way round. Every production deployment has a governed corpus with lineage back to source systems, a retrieval layer that enforces the same entitlements as the databases the content came from, a model gateway with routing, rate limits, and cost controls, an evaluation harness that runs before every change, and audit logs that record what was retrieved, what was generated, and for whom. That is the contract our generative AI consulting engineers and operates.
The practice sits on top of our vector data engineering, data governance, and MLOps work and alongside our Data Science & AI consulting. Most enterprise generative AI programmes stall for reasons that have nothing to do with the model: stale or duplicated content in the index, retrieval that ignores permissions, no way to tell whether an answer got better or worse after a prompt change, and token bills nobody forecast. Our engineers have operated the databases, search engines, and streaming platforms beneath these systems for two decades, so those failure modes are designed out before the first user sees the interface.
Generative AI Consulting Services, End to End
Six engineering domains, delivered as a bounded programme, embedded engineering, or 24×7 managed AI operations.
AI Strategy & Use-Case Portfolio
Generative AI consulting begins with an inventory of candidate use cases scored on value, data readiness, risk, and the cost to operate, then sequenced on a ladder from assist (drafting, summarising, search) through augment (recommendation with a human in the loop) to automate (agentic workflows with guardrails). Build-versus-buy and model-hosting decisions are made on measured requirements, not on vendor roadmaps.
Private RAG & Knowledge Platforms
Generative AI consulting at the data layer: retrieval-augmented generation engineered as a data pipeline: document and database ingestion with lineage, chunking and embedding strategies chosen from retrieval evaluation rather than defaults, hybrid vector and keyword search on Milvus, pgvector, ClickHouse, or OpenSearch, and entitlement-aware retrieval that applies row-level and document-level permissions before anything reaches the model.
Agentic Workflows & Orchestration
Generative AI consulting for agents: multi-step agents that call tools, query databases, and act in business systems, built with explicit state, bounded autonomy, idempotent actions, and approval gates for anything irreversible. Orchestrated with LangGraph, Semantic Kernel, or purpose-built services, and instrumented so every step is traceable and replayable.
LLMOps, Evaluation & Guardrails
Generative AI consulting is only credible with evaluation: sets built from real queries and expert-labelled answers, automated scoring of groundedness, relevance, and policy compliance on every prompt, model, or index change, prompt and configuration versioning, input and output guardrails for PII, injection, and off-policy content, and canary rollouts with rollback. Delivered with our MLOps consulting practice.
Model Gateway, Hosting & FinOps
Generative AI consulting for the platform: a single gateway in front of self-hosted open-weight models (vLLM, TGI, Triton on your GPUs) and cloud model APIs, with routing by task, caching, rate limits, per-team budgets, and cost per query reported alongside latency. Sizing of GPU capacity and inference batching from measured traffic, coordinated with our cloud FinOps practice.
AI Governance, Security & Compliance
Generative AI consulting for the risk owner: data residency and in-VPC deployment, classification-aware corpus admission, audit logging of retrieval and generation, human-review workflows, model and system cards, and controls mapped to GDPR, India DPDP, HIPAA, SOC 2, and the EU AI Act's risk tiers. Coordinated with our data governance consulting and database security practices.
The Private Generative AI Platform We Engineer
Vendor-neutral by principle. We run open-weight models on your GPUs or route to cloud model APIs, and we will say which is cheaper and safer for each use case.
The corpus is a governed dataset. In our generative AI consulting method, documents and database content enter the index through the same pipelines, lineage, and quality checks as any other dataset, with classification carried on every chunk. Deletions and permission changes propagate to the index within an agreed freshness target, so a revoked document stops being retrievable on time.
Retrieval enforces permissions, the model does not. Entitlements are applied as filters in the vector and keyword search, using the same role and row-level rules the source databases enforce. A model can only leak what it was given, so we control what it is given.
Every change is evaluated before it ships. A new prompt, model version, chunking strategy, or reranker is scored on the evaluation set against the current champion, on groundedness, relevance, refusal behaviour, latency, and cost, before a canary sees production traffic.
- Hybrid retrieval (dense vectors plus BM25) with reranking, chosen from measured retrieval precision
- Embedding model changes handled as re-index migrations with dual-write and cut-over, never in place
- Prompt templates, tools, and guardrail policies versioned in a repository and promoted through environments
- Retrieval and generation logs in ClickHouse with query, chunks, model, tokens, latency, cost, and user
- Per-use-case budgets with alerts on cost per query and on token growth
- Rehearsed failure modes: model endpoint outage, index corruption, prompt injection, entitlement drift
How Generative AI Consulting Sequences Value Against Risk
The right first use case is the one that pays back quickly while the governance, evaluation, and retrieval foundations are proven. Autonomy is earned, not assumed.
Vector Stores: Milvus, pgvector, ClickHouse
Generative AI consulting has to treat the vector store as a database, and its index type, quantisation, and filtering strategy decide both recall and cost. We choose Milvus for large multi-tenant corpora with heavy filtering, pgvector where the corpus lives beside transactional data and row-level security must apply, and ClickHouse where hybrid search over logs and structured data matters. Index parameters are tuned from measured recall at the latency the use case needs. See our vector data engineering practice.
Model Hosting: Self-Hosted Open Weights or Cloud APIs
Self-hosting open-weight models on your GPUs gives residency, predictable cost at volume, and control over versions; cloud model APIs give frontier capability and zero capacity management. Our generative AI consulting puts both behind one gateway, routes by task and sensitivity, and measures cost per query and quality per use case so the decision is revisited on evidence each quarter.
Evaluation and Audit on ClickHouse
Generative AI consulting without logs is guesswork. Every request, retrieved chunk set, prompt version, model, generated answer, token count, latency, and user is logged to ClickHouse. Evaluation scores, cost trends, and policy-violation rates become scheduled analytical queries, and the compliance question about what a user was shown on a given date is answered in seconds. Our ClickHouse consulting practice operates this layer.
Four Ways to Work With Our Generative AI Consulting Team
From a bounded strategy and readiness assessment to fully managed AI operations under a 24×7 SLA.
| Model | Best for | What you receive |
|---|---|---|
| GenAI Strategy & Readiness Assessment | Leadership teams deciding where to invest, or programmes with pilots that have not reached production | Scored use-case portfolio on the assist/augment/automate ladder, data and governance readiness findings, hosting and cost model, risk classification, and a sequenced roadmap |
| Private RAG or Agent Build | A specific use case to take to production inside your VPC with entitlements, evaluation, and audit | Corpus pipelines, vector and hybrid retrieval, model gateway, application or agent, evaluation harness, guardrails, Infrastructure-as-Code, and runbooks |
| Managed AI Operations | Lean teams that need 24×7 coverage for indexes, gateways, models, and the databases behind them | SLO-backed monitoring of groundedness, retrieval precision, latency, cost, and policy compliance; incident response under our severity matrix; monthly quality and cost reviews |
| Embedded AI Engineering | Sustained programmes across many use cases, teams that want capability transfer | Senior AI and data engineers integrated with your cadence, with evaluation sets, prompts, and documentation as standing deliverables |
Generative AI Operated to the Same Standard as Production Databases
An assistant that quietly starts answering from stale documents, or whose token bill doubles in a month, is an incident. We run generative AI systems with the operating discipline of our 24×7 Remote DBA practice.
Under our generative AI consulting SLA, every managed use case carries five SLOs: groundedness (share of answers supported by retrieved evidence, scored continuously on sampled traffic), retrieval precision (relevance of the chunks the model was given), latency (p95 end to end including retrieval and generation), cost per query against budget, and policy compliance (guardrail violation rate and entitlement checks). Corpus freshness and index health are monitored as leading indicators.
Incidents follow the same severity model as our database support: a customer-facing assistant returning answers from another tenant's documents is an S1 with a 15-minute response target; an internal knowledge assistant with a freshness breach is an S2. Every incident closes with a root-cause analysis and a preventive action. Quarterly, we rehearse the failures that matter to generative AI systems: model endpoint outage, embedding model deprecation, index corruption, prompt injection, and entitlement drift after a source-system permission change.
- 24×7 monitoring of gateway latency, error rate, token spend, GPU saturation, and index health
- Groundedness and relevance scored on sampled production traffic, trended per use case
- Corpus freshness and deletion propagation verified against source systems
- Guardrail violation and refusal rates reviewed monthly with use-case owners
- Model and embedding version lifecycle managed with evaluated, reversible migrations
- Runbooks with verification before and validation after every prompt, model, or index change
Generative AI Consulting Across Regulated and Data-Intensive Industries
The use cases that reach production first are the ones with a governed corpus, a clear human role, and a measurable outcome.
Banking, Payments & FinTech
Policy and procedure assistants, KYC and onboarding document intelligence, complaint triage, and analyst copilots over governed data, with audit trails regulators can read. See data engineering in banking and FinTech.
Healthcare & Life Sciences
Clinical documentation support, literature and protocol assistants, and HCP engagement summaries under HIPAA-class controls with de-identified corpora and full retrieval audit.
Retail & Consumer Goods
Conversational product discovery grounded in the catalogue and inventory, merchandising and planner copilots, and customer-service assistants with order-system tools. See our retail data architecture practice.
SaaS & Technology
In-product assistants with tenant-isolated retrieval, support deflection over documentation and tickets, and engineering copilots over internal code and runbooks. See data engineering for SaaS.
Manufacturing & Supply Chain
Maintenance and procedure assistants over manuals and sensor history, supplier document intelligence, and planner copilots that query the ERP through governed tools.
Insurance
Claims document intelligence, underwriting assistants over policy and risk data, and agent-facing copilots, with privacy controls on health and financial attributes.
Why Enterprises Choose MinervaDB for Generative AI Consulting
We engineer the data the model depends on
Most generative AI consulting starts at the prompt. Our engineers build and operate the corpus pipelines, vector stores, entitlement rules, and audit logs beneath the model, on the same PostgreSQL, ClickHouse, Milvus, and Kafka platforms they run for our clients' core systems, so the whole path is one accountable team.
Vendor-neutral, measurement-driven
We sell no model, platform, or vector database licences and earn no referral fees. Every recommendation names the evaluation result, log, or system table that justifies it, and we will tell you when a cloud API beats self-hosting, when keyword search beats vectors, and when a use case is not ready for autonomy.
Generative AI consulting that is private by design
Deployments run inside your VPC or data centre. Prompt, model, and index changes are evaluated, staged, and reversible; agent actions that are irreversible carry approval gates; and every procedure states its blast radius and rollback path before it runs.
Knowledge transfer by default
Evaluation sets, prompts, retrieval configuration, guardrail policies, and runbooks are documented and handed over. Your team should be able to operate what we build; if they choose to have us keep operating it, that is a decision, not a dependency.
"An enterprise assistant is only as trustworthy as the data it retrieves, the permissions it respects, and the evidence it leaves behind. The model is the easy part."
— The MinervaDB AI Engineering TeamHow a Generative AI Consulting Engagement Runs
Discover
Use-case inventory and scoring, corpus and permission mapping, data readiness and governance findings, hosting constraints, and regulatory classification.
Design
Target architecture, retrieval and model strategy, evaluation set and SLO targets, guardrail policy, cost model, and a staged plan with rollback at every phase.
Build & Validate
Pipelines, retrieval, gateway, application or agent, and evaluation harness delivered as code; red-team and entitlement testing; canary on real traffic before general release.
Operate & Improve
SLO-governed AI operations, monthly quality and cost reviews, evaluated model and index upgrades, and knowledge transfer until your team owns the platform.
Generative AI Consulting: Frequently Asked Questions
What does MinervaDB's generative AI consulting include?
AI strategy and use-case portfolio, private RAG and knowledge platforms, agentic workflows, LLMOps with evaluation and guardrails, model gateway, hosting and FinOps, and AI governance, security and compliance. Each can be delivered as a bounded assessment, a production build, embedded engineering, or 24×7 managed AI operations.
Can generative AI run entirely inside our own environment?
Yes. Our generative AI consulting defaults to it: open-weight models served with vLLM, TGI, or Triton on your GPUs, vector stores on Milvus, pgvector, or ClickHouse, and all logs and evaluation data stay in your VPC or data centre. Where a cloud model API is the better choice for a use case, it sits behind the same gateway with data-handling terms, redaction, and logging you control.
How do you stop an assistant from leaking data users are not entitled to see?
In our generative AI consulting method, entitlements are enforced at retrieval, not by the model. The same role, tenant, and row-level rules the source databases apply are carried as metadata on every chunk and applied as filters in vector and keyword search, so the model only ever receives content the user could already read. Permission changes and deletions propagate to the index within an agreed freshness target, and every retrieval is logged.
How do you measure whether a generative AI system is working?
Generative AI consulting has to prove itself with an evaluation set built from real queries and expert-labelled answers, scored automatically for groundedness, relevance, refusal behaviour, latency, and cost on every change, and with the same metrics sampled from production traffic and trended per use case. Business outcome measures, such as handling time or deflection rate, are captured alongside so value is proven rather than asserted.
Should we start with agents or with retrieval-augmented generation?
Usually with retrieval, because a governed corpus, entitlement-aware search, and an evaluation harness are prerequisites for any agent that acts on your data. Our generative AI consulting sequences use cases on an assist, augment, automate ladder so autonomy is introduced once the controls beneath it are proven, and we say so when a workflow is not ready for it.
How does this relate to your data governance, MLOps, and decision intelligence services?
Data governance consulting makes the corpus trustworthy; MLOps consulting supplies the evaluation, promotion, and monitoring discipline; decision intelligence connects AI outputs to measured business decisions. Generative AI consulting builds on all three, which is why we deliver them as one team.
Let's Take Generative AI From Pilot to Production, Privately
Talk to a MinervaDB principal AI engineer about the use case, the corpus, or the governance question that is holding your programme back. The first conversation is always with an engineer, never a salesperson.
Schedule a Consultation → Download the MinervaDB Corporate Flyer (PDF) →Generative AI Resources from MinervaDB
- Data Science & AI Consulting
- Vector Data Engineering
- Data Governance Consulting
- MLOps Consulting
- Decision Intelligence Consulting
- Data Engineering Consulting and Managed Pipelines
- Cloud Database Optimisation and FinOps
- Retrieval-augmented generation (Wikipedia)
- Milvus documentation
- vLLM documentation