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Data & AI Engineering Studio

Data and AI engineering, from raw source to agents in production.

Six capabilities across five layers: four that build from raw data to agents, and governance across all of them.

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See the six capabilities →

What we see blocking AI adoption

Pick the one that sounds familiar.

500+

Engagements since 2014

200+

AI-native engineers

30+

Snowflake certified professionals

40+

Claude-certified architects

3+

Agentic frameworks

The CriticalRiver approach

Each layer only works
if the one below it does.

Adoption is no longer the differentiator. What separates enterprises now is whether an AI-driven decision can be explained, evidenced and defended. In our delivery work, that almost never comes down to the model. It comes down to whether the data behind the decision is traceable, its meaning is agreed, and every step is on record.

Platform-led, not platform-locked

  • Runs on your data platform
  • Deploys in your cloud
  • Stays inside your compliance boundary
Read the stack by
Conformed tables
Certified metrics
Scores and grounded answers

Foundation

The facts

Data Platform
Engineering & Modernization

Context

The meaning

Where most estates stall
Semantic Layer
& Enterprise Context

Intelligence

The judgment

Analytics &
Decision Intelligence

AI/ML Engineering &
MLOps

Agents

The action

Agentic
Engineering & AgentOps

Governance, across every layer

The evidence

Data Strategy,
Governance & Evidence

Where the gaps cluster is where the money should go first. It is almost never the model layer.

Find your blocking layer
Explore the six capabilities
CAPABILITIES

Six capabilities across the stack

Each one fixes a specific layer and is engineered to the patterns the leading data platforms are converging on. Open a capability to see how we build it, where it has been proven, and the IP behind it.

Data Platform Engineering & Modernization

Target architecture, a modernized platform, and lineage to every figure.

Foundation

Every AI answer is only as good as the number underneath it. We design the target architecture first, then modernize the platform so every figure traces back to one reconciled source.

How we build it
  • Medallion layers, raw to conformed to curated, with lineage at every hop
  • Open table formats, so storage is not tied to a single engine
  • CDC and streaming ingestion alongside batch ELT
  • Data contracts and quality checks enforced inside the pipeline
  • Workload isolation and cost controls set per domain
OutcomesFinance close platform
AcceleratorsModernyx

This is where most estates stall. We define metrics, entities and relationships once, certify them with the people who own them, and make that layer the only path BI, models and agents use.

How we build it
  • Metrics, dimensions and joins defined once as governed objects in the platform
  • Ontology and entity resolution across source systems
  • Master and reference data with named owners
  • Hybrid vector and keyword retrieval over governed content
  • Business vocabulary and synonyms published so agents read terms correctly
OutcomesOne foundation, two AI tracks
AcceleratorsSemanticForge

A dashboard is not a decision. We build analytics that rank what matters, recommend the next action, and show the records behind every recommendation.

How we build it
  • Natural language questions resolved against certified metrics, not raw tables
  • Threshold and anomaly detection across the full estate
  • Findings ranked by impact, each paired with a recommended action
  • Self-service BI on the same semantic layer the agents use
OutcomesDashboards to decisions
AcceleratorsLexora

Models are judged on your data, not on a benchmark. Each one ships behind an evaluation gate, runs inside your perimeter, and is watched for drift after release.

How we build it
  • Feature pipelines on governed data, reused across models
  • Model registry, lineage and reproducible training runs
  • Evaluation sets and LLM-as-judge scoring on your own data before release
  • Drift, quality and cost monitoring in production
  • Model calls routed inside your network perimeter
OutcomesOne foundation, two AI tracks
AcceleratorsAgent Validation Framework

Agents that change systems need more than good prompts. The user’s own identity reaches the data, every plan is authorized before it runs, and every action leaves a record.

How we build it
  • Intent classification and multi-agent routing with a defined fallback
  • On-behalf-of identity, so the user’s own role reaches the data
  • Tools exposed over MCP with scoped, audited permissions
  • Typed plans authorized before execution, with human approval on writes
  • Tracing and certification gates before an agent takes traffic
OutcomesCertified agent layer
AcceleratorsMeridianNeuralOps

Governance works as instruments, not committees. Ownership, access policy, lineage and model risk controls run inside each layer, so any decision can be explained after the fact.

How we build it
  • Attribute-based access, row filters and column masking in the query path
  • Lineage from source record to answer, including agent calls
  • Catalog-driven ownership and certification of data and agents
  • Evaluation evidence and model risk records for every release
  • A stewardship model that runs governance as an instrument
OutcomesGoverned connector factory
AcceleratorsClaudeMeshAgent Validation Framework
Outcomes and evidence

Every layer, proven with our enterprise partners

Each engagement began at the layer holding our partner back and built up from there: a governed data foundation first, agents last. Here is what that looked like at each layer of the stack.

Foundation
Technology, local commerce

A finance platform the close runs on

  • A medallion platform unifying several ERP sources into one governed base
  • A semantic layer with one agreed definition per metric
  • Finance agents and forecasting built last, on the same foundation
30 to 40%faster financial close
60%less manual effort in the cycle
95%data trust across reported metrics
Partner-reported figures

A faster close is a data problem before it is an AI problem.

Context
Software, contract lifecycle

One governed foundation, two AI tracks

  • One governed semantic layer under every delivery wave
  • Agentic retrieval and trained predictive models on the same base
  • Model scores fed to agents as context, inside the partner’s own perimeter
5delivery waves on a single foundation
2AI tracks sharing one semantic layer
In-VPCmodel calls, with no egress

Five retrieval builds would have given five definitions of the same metric. One semantic layer gives one.

Intelligence
Semiconductor, media operations

From dashboards to decisions, with the action attached

  • One unified performance model across campaigns, keywords and geography
  • Every finding delivered with a specific, time-bound action
  • Waste discovery against configurable thresholds
City levelspend concentration surfaced automatically
Every cardtraced to the campaigns behind it
One promptreturns a structured, graded breakdown

The product is not the insight. It is the action attached to it.

Agents
Software, regulated reporting

A certified agent layer over a governed warehouse

  • A master agent that routes intent to the sub-agent owning each domain
  • A certification gate read live from the metadata catalog
  • Column masking enforced against the caller’s own role
>95%evaluation score required to certify an agent
4intent domains routed, with a fallback
Per rolemasked responses on the same query

An agent whose quality drops loses its traffic without a ticket.

Governance
Software, cloud contact center

A governed factory for enterprise AI connectors

  • A five-phase pipeline from ranked backlog to production validation
  • A security evaluation against the design before any build begins
  • A forward deployed engineer embedded as delivery lead
100%of connectors security-reviewed pre-build
7named roles across two organizations
~1,000daily users on the first tranche

Nothing is built before the design has passed security.

Frameworks and accelerators

Reusable IP we bring to every engagement

A funded portfolio we build and harden ahead of any engagement, then configure to your estate for outcome-driven programs. An accelerator is a head start, not a shortcut.

  • Configured to younot a generic instance
  • Deployed in your estateyour cloud, your keys, lower environments first
  • Evaluated on your databefore anything touches a live system
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Agent orchestration framework

Meridian

Governed multi-agent orchestration over a semantic layer. The person’s own identity reaches the data, every request is planned before it runs, and every answer can be reconstructed.

Details
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Agent platform

ClaudeMesh

The governed AI operating layer: skills, tool access, policy, audit and cost visibility in one place.

Details
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Context

SemanticForge

Schema-driven document and content intelligence: extraction, semantic search and governed agent access.

Details
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Analytics

Lexora

The agentic data analyst: natural language to insight over a governed semantic layer, with every answer citing its records.

Details
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Agent operations

NeuralOps

Drift detection, monitoring and approval-gated remediation for agents and platforms in production.

Details
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Assurance

Agent Validation Framework

Model and agent risk: evaluation sets, groundedness checks and the audit trail regulated buyers ask for.

Details
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Modernization

Modernyx

Application and database intelligence before migration: surfaces the business rules buried in code and schemas, so the logic survives the move.

Details
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Enablement

AI Boot Camp

Role-based adoption built on each department’s own workflows, measured against a baseline taken before it starts.

Details
Full portfolio

Explore the accelerators

How each one works and where it fits. New frameworks and accelerators join the portfolio each quarter.

Open the portfolio
Questions we get asked

Enterprise data and AI, in detail

Something else on your mind?

Ask the team →

How do you make enterprise data AI-ready?

By fixing the facts before the meaning, and the meaning before the models. AI-ready data means a single traceable source for any figure, reconciliation that is automated rather than manual, and a governed model over the top that defines what each metric means. We assess against the five layers, foundation, context, intelligence and agents, with governance across all four, and name which one is blocking before recommending any platform.

How do you build a semantic layer on a platform we already run?

Without replatforming. We model meaning over the warehouse you run, Snowflake, Databricks, Fabric or BigQuery, certify the metrics with the business owners who define them, and make that layer the query path agents and BI use. Retrieval then runs against governed definitions rather than raw tables, which is what makes citation-grounded answers possible.

How do you determine the right AI architecture?

By finding which layer is failing before choosing any technology. An architecture assessment reviews the estate against the five layers, names the gap blocking production, and costs the options. Two to three weeks, ending in a written architecture position and a costed shortlist.

How do you prevent AI hallucination in enterprise systems?

With enforcement outside the model, not with better prompts. Retrieval is scoped to a governed semantic layer, so the model answers from certified definitions rather than raw tables. Output is citation-grounded, with every claim traceable to the records behind it. A validation layer tests groundedness and catches invented information before it reaches a user, judging the path the agent took as well as the answer.

How do you control AI and agent costs?

By treating cost per operation as a design target rather than a monthly surprise. Warehouse cost and performance tuning is part of the foundation build. Model calls run in-VPC with no egress where architecture and data classification allow. Spend is metered per user against individual keys rather than pooled at the account, and autonomy is scaled to the outcome.

How do you enable agent-to-agent communication?

Through declared contracts and a certification gate, not through prompts. A master agent routes intent to certified sub-agents, with certification status read from the metadata catalog before an agent can receive traffic. Interoperability runs on MCP, A2A, agent cards and registries, so an agent added later does not require the existing fleet to be rewritten.

How does this connect to application and product work?

Our Digital Engineering studio builds the products, applications and experiences the intelligence runs inside. You engage one team and one accountable lead whichever side the work sits on.

How to begin

Three ways to start, none of them a program.

Each is bounded, priced up front, and ends in a decision you make on evidence. Pick the one closest to where you are, or leave it to us and we will recommend it.

Let’s start a conversation.

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