MinervaDB · Data Science & AI Consulting · real-time analytics platforms, production machine learning, private generative AI, 24×7 operations
Data Science & AI Consulting Built on Database-Grade Engineering
Data Science & AI Consulting at MinervaDB pairs principal-level machine learning delivery with the production database engineering that most AI programmes lack. We build the real-time data platform, the feature pipelines, the models and the private generative AI layer as one system in your own cloud accounts, measure every claim from the system tables, and then run it around the clock with named engineers. The firms that understand models rarely understand storage engines, query planners and streaming ingestion at scale; we have run those for 900+ enterprises, and this practice adds the other half.
The core problem
Why enterprise AI programmes stall, and what Data Science & AI Consulting has to fix first
Boards have mandated AI and budgets have moved, yet most data science and machine learning initiatives never reach production. The bottleneck is rarely the algorithm. It is the storage engine, the query planner, the streaming ingestion and the governed pipeline beneath the notebook that decide whether a promising model becomes a dependable product. Data Science & AI Consulting that starts at the model is starting in the wrong place.
Models without foundations
The first thing Data Science & AI Consulting has to repair: data science teams build on slow, ungoverned platforms; features are recomputed by hand, labels leak from the future, and nothing survives contact with production traffic.
Analytics bill shock
Warehouse-first architectures priced for periodic reporting, now serving event-heavy, real-time workloads at a monthly cost that finance has started to question.
GenAI privacy deadlock
Regulated enterprises want large language model capability but cannot send customer data, contracts or clinical notes to a third-party API, so pilots stop at the security review.
Pilot purgatory
The reason most Data Science & AI Consulting buyers call us: proofs of concept that never industrialise because no one owns pipelines, monitoring, retraining and operations end to end, and each handover loses context.
Who the Data Science & AI Consulting market offers, and where each one stops
Global system integrators bring generalist delivery pyramids: strong strategy decks, weak database internals, and costs that balloon once the work reaches the platform. Boutique machine learning shops are often excellent at modelling and hand off at the notebook, because they cannot operate production infrastructure. Database consultancies own the infrastructure and stop at the database, with no machine learning, no analytics narrative and no AI offer. Cloud-vendor professional services give architecture advice optimised for the vendor's consumption rather than the client's economics.
Production AI is a database engineering problem wearing a machine learning costume. Our Data Science & AI Consulting practice exists because MinervaDB already owned the hardest half of that problem, running mission-critical PostgreSQL, ClickHouse, MySQL and cloud data platforms, and the other half, the modelling and the MLOps, is the part that is easier to add to an infrastructure practice than the reverse.
| Capability | Global SIs | ML boutiques | Database consultancies | MinervaDB |
|---|---|---|---|---|
| Database internals and performance engineering | Weak | Weak | Strong | Strong, 15+ years of practice |
| Production ML and MLOps | Mixed | Strong | None | Core offer |
| Private, in-perimeter generative AI | Rare | Mixed | None | Core offer |
| 24×7 managed operations | Costly | None | Strong | Strong, existing muscle |
| Senior-only, principal-led delivery | No | Sometimes | Sometimes | Always |
| Cost-takeout economics that fund the work | No | No | Partial | Signature motion |
Portfolio
The Data Science & AI Consulting portfolio: six services, one accountable team
Three services form the engineering spine and three form the intelligence layer. Every engagement is scoped so that the platform work produces reusable assets, reference architectures, audit tooling and accelerator code, and every build is designed with a managed-operations attach from the first week.
Real-time analytics platforms
Architecture, build-out and optimisation of ClickHouse and PostgreSQL analytics stacks: streaming ingestion with Kafka, Debezium CDC and dlt; MergeTree data marts and materialized views; a semantic layer for BI; and warehouse cost-takeout migrations measured from system.query_log before and after. The anchor offer of our Data Science & AI Consulting practice and the platform every other service runs on.
Data engineering and MLOps
The operational backbone of Data Science & AI Consulting that turns models into products: dbt and Airflow or Dagster pipelines with data contracts, quality frameworks with Great Expectations, point-in-time feature pipelines, experiment tracking and a model registry on MLflow, CI/CD for models with shadow and canary promotion, and drift monitoring wired to retraining triggers.
Analytics strategy and audits
The Data Science & AI Consulting entry point: fixed-scope, fixed-fee assessments of two to four weeks that open most relationships: platform performance and cost review from the system tables, data architecture and governance assessment, and a descriptive-to-predictive-to-prescriptive roadmap with use cases scored for value and feasibility.
Applied machine learning
The Data Science & AI Consulting outcome most clients buy first: production models for revenue and risk, deployed rather than left in notebooks: hierarchical demand forecasting at SKU, store and day grain; customer analytics with RFM, lifetime value and churn; pricing, promotion and markdown optimisation; fraud and anomaly detection on streaming data with millisecond feature reads.
Private and local generative AI
Large language model capability that never leaves your perimeter: retrieval-augmented generation over governed enterprise data, in-VPC model serving and fine-tuning on open-weight models, AI copilots wired to the semantic layer, and an evaluation harness with guardrails and observability. Built on our Vector Data Engineering practice.
Managed data and AI operations
Data Science & AI Consulting does not end at go-live: 24×7 operations for everything we build: platform SRE for uptime, performance and upgrades; pipeline and model operations including retraining and drift response; cost governance and capacity planning; and quarterly optimisation and roadmap reviews under the same severity matrix as our database support.
Engine depth behind the portfolio: ClickHouse, PostgreSQL, MySQL, BigQuery, Snowflake, Databricks and Redshift, plus dedicated MLOps, generative AI and decision intelligence engagements.
Reference architecture
One reference architecture, industrialised for each Data Science & AI Consulting client
Rather than reinventing the stack per client, we industrialise a single proven architecture and tailor it to the workload. The decisions that change from client to client are the engine, the ingestion pattern, the feature freshness and the model serving latency; the discipline around them does not.
Client sources, transactional databases, product and web events, enterprise systems and files in the lake, flow through streaming and batch ingestion into a real-time analytical store. On ClickHouse that store is a set of MergeTree tables partitioned by time with materialized views maintaining the marts; on PostgreSQL it is partitioned tables with incremental dbt models and, where the workload calls for it, a columnar extension. Either way, raw events, marts, feature tables, a semantic layer and governance live together, so that BI, machine learning and language-model tools read the same definitions.
Four consumption paths open from that foundation. SQL-native analytics handle funnels, cohorts, RFM and anomaly detection without a machine learning stack. A Python layer performs feature engineering in the database and trains models with XGBoost, LightGBM, Prophet or PyTorch, writing scores and forecasts back for millisecond serving. A private generative AI layer runs retrieval-augmented generation and in-VPC language models over governed data. The consumption tier delivers dashboards, APIs, copilots and real-time personalisation.
What makes this Data Science & AI Consulting architecture different from a diagram in a vendor deck is the cross-cutting layer: platform SRE with query latency, merge health and ingestion lag measured from system.query_log and pg_stat_statements; data quality with contract checks on arrival and freshness SLOs per table; model operations with a registry, drift monitors and scheduled retraining; and cost governance with storage tiering and query-cost attribution per team. Every one of those is instrumented before the first model ships, because a platform that cannot be observed cannot be operated.
Engine choice is a measurement, not a preference. We are vendor-neutral by principle: a Data Science & AI Consulting engagement can end with ClickHouse, PostgreSQL, a cloud warehouse the client already runs, or a mix, and the audit says which, with the numbers behind it.
Figure 1. The reference architecture we industrialise per client. Scores, forecasts and embeddings are written back to the store so that analytics, applications and language-model tools consume one governed dataset.
Data foundations
Pipelines, contracts and features: the engineering beneath the models
The failure mode we see most often in Data Science & AI Consulting rescues is not a bad model but a feature that meant one thing at training time and another in production. The foundations below exist to make that impossible by construction.
Ingestion that can be replayed: the first Data Science & AI Consulting foundation
Change data capture with Debezium from PostgreSQL logical replication, MySQL binlog or MongoDB change streams lands every insert, update and delete in Kafka with the log position attached, so any downstream table can be rebuilt from an offset. Batch extracts run through dlt or Airbyte into dbt models orchestrated by Airflow or Dagster, and every load is idempotent: re-running yesterday produces yesterday, not a duplicate. Kafka 4.x runs KRaft-only, which removes the ZooKeeper tier from the operational surface; we plan upgrades of older clusters into that window.
Data contracts at the boundary of every Data Science & AI Consulting pipeline
Each source has a contract: schema, freshness, volume and null-rate expectations enforced with Great Expectations as data arrives. A failing contract stops the run and pages the owner rather than silently training a model on a half-loaded day. Lineage from source column to model feature is recorded so that a schema change upstream is traceable to every model it touches.
Features defined once, served twice: the Data Science & AI Consulting feature standard
A feature is one definition in SQL or Python, versioned in git with an owner, an entity key, an event-time column and a freshness SLO. The offline lane materialises it with point-in-time joins, ASOF JOIN on ClickHouse or a LATERAL window on PostgreSQL, so that no feature reads data timestamped after its label. The online lane computes the same definition incrementally on arrival, in materialized views or Flink, into a Redis, Valkey or PostgreSQL row store keyed by entity. Value skew, time skew, label leakage and coverage are checked continuously and alert before the model's metrics do.
-- ClickHouse 25.8 LTS: point-in-time features for a churn label.
-- ASOF JOIN picks the latest feature snapshot at or before label_ts,
-- so training can never see the future.
SELECT l.customer_id,
l.label_ts,
l.churned,
f.orders_30d,
f.avg_basket_30d,
f.support_tickets_90d
FROM churn_labels AS l
ASOF LEFT JOIN customer_features AS f
ON f.customer_id = l.customer_id
AND f.feature_ts <= l.label_ts
WHERE l.label_ts >= toDate('${TRAIN_FROM}')
AND l.label_ts < toDate('${TRAIN_TO}');
Figure 2. The feature pipeline with one definition feeding both lanes. Training-serving skew is removed structurally rather than detected after the fact, and the checks that remain run continuously.
Applied ML and MLOps
Applied machine learning that reaches production and stays there
We focus the Data Science & AI Consulting practice on use cases where the data shape is well understood, the business metric is measurable and the serving path is engineered rather than improvised. The table states the patterns we deploy most, and the loop below is how they are kept honest after launch.
| Use case | Data shape | Model families we deploy | Serving path | Success metric |
|---|---|---|---|---|
| Demand forecasting | SKU × store × day, promotions, calendar, weather | Gradient-boosted trees with lag features, Prophet for seasonality, hierarchical reconciliation | Nightly batch to the store; forecasts joined in the semantic layer | WAPE by hierarchy level against a naive seasonal baseline |
| Churn and lifetime value | Customer events, orders, support, product usage | XGBoost or LightGBM survival and classification models; RFM segmentation | Daily scores written back; online features for in-session triggers | Uplift on retained revenue in a held-out cohort |
| Pricing and markdown | Price history, elasticity, inventory, competitor feeds | Elasticity models with constrained optimisation; bandits for promotion allocation | Weekly optimisation with guardrails; API for exceptions | Gross margin per unit under sell-through constraints |
| Fraud and anomaly detection | Transaction streams, device and session signals, graph features | Gradient boosting on streaming features; isolation forests; graph features on entity links | Real-time scoring in the request path with a p95 budget | Recall at a fixed false-positive rate; analyst time per alert |
| Product analytics | Event streams, funnels, cohorts, feature flags | Sequence models where warranted; most value from SQL-native cohorts and experiments | Dashboards and experiment readouts from the semantic layer | Decision latency and experiment throughput |
Model training that is reproducible in every Data Science & AI Consulting build
Every run logs parameters, metrics, the data hash and the code commit to MLflow 3.x; a candidate model is versioned in the registry with lineage back to the training set. Promotion to production requires the offline metric to beat the champion on fresh labels, stability and fairness checks, contract tests on the API, a load test at the agreed p95 budget and a reproducibility check that retrains from the logged inputs. None of that is optional in a Data Science & AI Consulting build, because a model that cannot be reproduced cannot be debugged.
Deployment that can be undone: the Data Science & AI Consulting promotion rule
New versions score in shadow against live traffic before they serve anything, then move to a canary at five to ten percent with automatic rollback on metric regression. Monitoring covers prediction drift, feature drift, the business KPI, latency and cost per thousand predictions on one dashboard, and drift, performance, schedule and data triggers close the loop back to retraining. Rollback is the previous registry version behind the same endpoint, with no retraining required to recover.
Figure 3. The MLOps lifecycle installed on build engagements and run under managed operations. Each promotion passes a gate and each model can be rolled back to the previous registry version.
Private generative AI
Private and local generative AI inside your perimeter
For regulated enterprises the question is not whether a language model can answer, but whether the prompt, the retrieved documents and the answer ever left the boundary. Our Data Science & AI Consulting practice deploys retrieval-augmented generation and model serving in the client VPC or data centre, with nothing leaving by default and a governed egress path where a use case justifies it.
The Data Science & AI Consulting topology for private generative AI is deliberately conventional. Users authenticate through your identity provider and their entitlements travel with every request. A gateway applies rate limits, PII detection and masking, prompt-injection screening and policy checks on both input and output. The RAG orchestrator rewrites the query, retrieves candidates through dense, lexical and filtered search inside the vector store, reranks them, assembles a prompt with citations and streams the answer from an in-VPC model served by vLLM or Text Generation Inference on your GPU nodes. Embeddings come from an in-VPC embedding service with the model version pinned on every vector.
The vector store is pgvector on PostgreSQL or Milvus, chosen by measurement, with tenant and ACL filters applied inside the engine so that ranking never sees a document the user may not read; the retrieval engineering behind that is described on our Vector Data Engineering page.
Quality in Data Science & AI Consulting is measured, not asserted. A golden question set with expected sources is built with your domain experts, and faithfulness, citation precision and refusal correctness are run as a regression on every model, prompt or index change. Every prompt, retrieved chunk, model version and answer is logged against the user identity, and deletion of a source document propagates to every chunk and every cached answer derived from it.
The design produces the evidence GDPR, DPDP, HIPAA and SOC 2 reviewers ask for about AI systems: a data-flow diagram, a model inventory, access logs, retention and deletion records and evaluation reports. Whether a use case may call an external model at all is a governance decision recorded per use case, enforced by an egress gateway with masking, logging and an allow list rather than by policy documents alone.
Figure 4. Private generative AI deployed inside the client perimeter. Model choice between open-weight and external is a governance decision per use case, and the compliance evidence is produced by the design rather than assembled afterwards.
Performance and cost
The economics: performance engineering that funds the Data Science & AI Consulting engagement
Warehouse-first stacks were priced for periodic reporting. Event-heavy, real-time analytics on the same stack pays for scanned bytes, idle compute and materialised copies that a columnar engine with the right sort keys does not need. We measure the difference on your own queries before anyone talks about migration.
The Data Science & AI Consulting audit begins with the query history. On a cloud warehouse that is the account usage or information-schema views; on ClickHouse it is system.query_log and system.parts; on PostgreSQL it is pg_stat_statements. From those we classify workload by pattern, dashboard reads, ad hoc analysis, scheduled transforms and model feature builds, and attribute cost to each. The most common finding is that a small number of dashboard queries scanning unpartitioned fact tables account for most of the bill, and that the same queries against a MergeTree table with a sort key matching the filter columns read a fraction of the data.
Where the numbers justify it, the migration is engineered as a dual-running cutover: the new store is loaded from the same CDC stream, queries are translated and reconciled against the old results, dashboards are repointed one at a time, and the old platform is retired only after a month of clean parity. Where the numbers do not justify it, the audit says so and the engagement ends with a tuned warehouse, which is a legitimate outcome of Data Science & AI Consulting and one we deliver often.
| What we measure | Where it comes from | Why it matters |
|---|---|---|
| Bytes scanned per query class | warehouse usage views; system.query_log | The primary driver of cloud warehouse cost and of ClickHouse CPU time |
| Query latency p50 / p95 / p99 | system.query_log, pg_stat_statements | Dashboards and feature builds have latency budgets; averages hide the tail |
| Ingestion lag and merge health | system.parts, system.merges, consumer offsets | Real-time analytics is only real-time if the data is |
| Storage by tier and compression ratio | system.parts, object storage inventory | Tiering cold partitions to object storage changes the cost curve |
| Cost per team and per model | query attribution tags, registry metadata | Chargeback makes optimisation someone's job |
We do not publish cost-savings percentages on this page. The reduction depends on the workload, and the audit produces the figure for yours from measured queries; we never quote a number we have not measured.
Managed operations
Managed data and AI operations: the platform is run, not just built
Everything we build is scoped with a managed-operations attach, because a model in production is a system that changes under load, data drift and upstream schema changes. The same 24×7 organisation that supports our database clients runs the Data Science & AI Consulting platforms.
Platform SRE
The operational half of Data Science & AI Consulting: uptime, performance and upgrades for ClickHouse, PostgreSQL and the ingestion tier; capacity forecasting from measured growth; rehearsed failover and restore with quarterly DR drills; upgrade planning against each engine's release calendar.
Pipeline and model operations
Contract failures, backfills and replays; retraining on drift, performance, schedule or data triggers; incident response for scoring outages; monthly review of every model's metrics against the values agreed at launch.
Cost and roadmap
Data Science & AI Consulting economics reviewed monthly: cost review with attribution per team and model; storage tiering and query optimisation as a standing activity; quarterly roadmap sessions that add use cases from the backlog in priority order.
| Severity | Definition for data and AI platforms | Acknowledgement |
|---|---|---|
| S1 | Platform down, ingestion stopped with customer impact, scoring endpoint returning errors, or data exposure suspected | 15 minutes, 24×7 |
| S2 | Latency or freshness outside SLO; model metric below the agreed floor; failover degraded | 12 hours |
| S3 | Non-urgent defects, tuning and capacity requests, pipeline changes | 24 hours |
| S4 | Advisory, roadmap and upgrade planning | 48 hours |
Industries
Where Data Science & AI Consulting delivers first
We lead with verticals that combine high-volume streaming data with clear, measurable machine learning use cases, and where a real-time analytical store changes the economics of the platform as well as the models on it.
| Industry | Typical data | First use cases | Platform pattern |
|---|---|---|---|
| Retail and e-commerce | Point of sale, orders, catalogue, web and app events, inventory | Demand forecasting, customer analytics, real-time funnels, markdown optimisation | ClickHouse marts for events and inventory; PostgreSQL for catalogue and orders; nightly forecasts written back |
| FinTech and payments | Transaction streams, device and session signals, ledgers, KYC records | Fraud detection, risk scoring, regulatory reporting on streaming data | Kafka CDC into ClickHouse for analytics; online feature store for request-path scoring; audit-grade lineage |
| SaaS and digital platforms | Product telemetry, usage and billing events, support and CRM | Product analytics, usage-based billing telemetry, churn and expansion models | Event store with a semantic layer; daily scores to the CRM; experiment readouts from the same tables |
| Healthcare and regulated services | Clinical and operational records, documents, device data | Private copilots over governed documents, capacity and demand models | In-VPC generative AI with evidence for HIPAA-class review; PostgreSQL with row-level security |
Sector pages on this site go deeper into banking and FinTech, SaaS and digital payments, and the wider data engineering and analytics platform engineering practices this Data Science & AI Consulting work draws on.
How we engage
How a Data Science & AI Consulting engagement runs
Three phases with a decision gate after each, so you can stop after the audit with a usable answer or continue into build and operations with the same engineers. Every engagement is delivered by principal-level engineers with no leverage pyramid, and everything we build lives in your accounts and repositories.
The Data Science & AI Consulting audit is fixed-scope and fixed-fee, two to four weeks, and produces a findings report with measurements, a target architecture, a prioritised use-case backlog and a fixed-price proposal for the build. Architect and build runs eight to sixteen weeks on milestone billing and delivers the platform, the pipelines, the first models and, where in scope, the private generative AI layer, with runbooks, dashboards and load and failover tests. Operate is a retainer with named engineers, a monthly measured SLI report and change windows executed under change control.
Hourly Data Science & AI Consulting work outside a fixed scope is available for tuning, reviews and second opinions. Rates and retainer levels are stated in the proposal after the scoping call, and we invoice against logged hours or agreed milestones, never against estimates.
Every Data Science & AI Consulting deliverable ships with reproducible environments, versioned data and models, documented runbooks and measured before-and-after numbers, so quality is demonstrated rather than asserted. There is no MinervaDB runtime in the request path and no dependency on us to keep the platform running; the retainer exists because most teams prefer to have the engineers who built it on call.
Standing caveat on all advice: test before applying to production, and maintain a robust disaster recovery posture. We write the rollback path before we write the change.
Figure 5. The engagement lifecycle. Each phase stands alone, each ends with a decision gate, and the client owns the code, the measurements and the runbooks at every gate.
FAQ
Data Science & AI Consulting: frequently asked questions
The questions we are asked most often before an engagement starts.
What makes MinervaDB Data Science & AI Consulting different from a machine learning boutique?
We combine principal-level machine learning delivery with 15+ years of production database engineering across ClickHouse, PostgreSQL, MySQL and the major cloud data platforms. Most consultancies own only one half of the production-AI problem; we own both, which is why our models reach production and stay there under 24×7 operations.
Can you deliver generative AI without sending our data to third parties?
Yes. Data Science & AI Consulting at MinervaDB deploys retrieval-augmented generation, embedding and language-model serving inside your VPC or data centre on open-weight models, with guardrails, evaluation and audit logging built in. External model APIs are reachable only through a governed egress gateway when a use case is approved for it, and the design produces the evidence GDPR, DPDP and HIPAA-class reviews require.
How does a Data Science & AI Consulting engagement begin?
Almost every relationship starts with a fixed-scope, fixed-fee analytics audit of two to four weeks. It measures platform performance and cost from the system tables, assesses data architecture and governance, scores candidate use cases for value and feasibility, and produces a roadmap and a fixed-price proposal you can act on with us or with any team.
Which platforms do you build on?
ClickHouse and PostgreSQL are the default real-time analytical stores in our Data Science & AI Consulting builds, chosen per workload by measurement. We also work on BigQuery, Snowflake, Databricks, Redshift and AlloyDB where a client already runs them, with Kafka, Debezium, dbt, Airflow or Dagster, MLflow, Great Expectations, vLLM and pgvector or Milvus around them. We are vendor-neutral and will recommend against a migration when the numbers do not justify it.
How do you stop training-serving skew and label leakage?
Every Data Science & AI Consulting build defines each feature once and generates it into both an offline lane with point-in-time joins and an online lane computed incrementally on arrival. Value skew, time skew, label leakage and feature coverage are checked continuously and alert before the model's metrics degrade, and every training run is logged with its data hash so it can be reproduced.
What happens after the model is live?
Data Science & AI Consulting managed operations cover platform SRE, pipeline and model operations and cost governance under a severity matrix with S1 acknowledgement in 15 minutes. Models are monitored for prediction and feature drift, business KPI, latency and cost, and retraining is triggered by drift, performance, schedule or data changes through the same promotion gates as the original launch.
Do you publish cost-savings figures for warehouse migrations?
No. Data Science & AI Consulting cost figures depend on the workload, and we never quote a number we have not measured. The audit classifies your query history by pattern, attributes cost to each class and models the same queries on the candidate engine, so the figure in the proposal is yours, measured, rather than a headline.
Which industries does the Data Science & AI Consulting practice serve first?
Retail and e-commerce, FinTech and payments, SaaS and digital platforms, and regulated services such as healthcare, because they combine high-volume streaming data with measurable machine learning use cases and, in the regulated cases, a hard requirement for in-perimeter generative AI.
Turn your data foundation into an AI advantage
Talk to a MinervaDB principal about your most demanding Data Science & AI Consulting challenge. The first conversation is with an engineer who has run production data infrastructure at scale, never a salesperson.