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Digital Engineering Studio

Digital engineering, where AI speeds up the whole delivery, not just the code.

Products, platforms, modernized applications and the pipelines that release them, built, tested and run by one team with AI in each stage and measured against your own baseline.

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Conversations we keep having

Recurring themes from early discussions with enterprise delivery teams.

  • A small team of engineers takes over the app, fixes the code and runs it in production.

    Product & Platform Engineering

  • Agents extract business rules from the code, each linked to its source, before migration is planned.

    Application Modernization & Integration

  • Security and policy checks run on every commit, so reviewers handle only what needs judgment.

    Cloud, DevSecOps & SRE

  • Tests generated from the live application and kept current, with a readiness score per build.

    Quality Engineering

  • We find where work waits longest, apply AI there first and measure the result.

    AI-Native Engineering

500+

Engagements delivered end to end since 2014

200+

AI-native engineering professionals

50 to 60%

of delivered code produced through AI-assisted engineering

10+

Accelerators embedded across the six stages

40+

Claude-certified architects

The CriticalRiver approach

One team, from the first
idea to a live product.

Discovery, design, build, modernization, integration, release and operations, on one backlog and one cadence. AI works inside each stage, and a named engineer is accountable for what it produces.

How we deliver

  • One accountable lead across every stage
  • Roadmap decisions tied to measured results
  • Named human review on AI-produced code
Read the lifecycle by

Discover and design

Product & Platform

Build

Product & Platform

Modernize

Modernization & Integration

Integrate

Modernization & Integration

Deploy

Cloud, DevSecOps & SRE

Run and assure

Quality Engineering

Runs under all six stages

AI across the lifecycle

AI-Native Engineering

The data-to-product handover, kept inside one team.

From raw data to a live product. Data & AI Engineering owns the data foundation, context and models. Digital Engineering owns the product and how it runs. The handoff between them, where most programs lose time, stays inside one team.

Data & AI Engineering

Data foundation

View the Data & AI page

Data & AI Engineering

Context and meaning

View the Data & AI page

Data & AI Engineering

Models and agents

View the Data & AI page

Digital Engineering

Product and platform

Product & Platform
Modernization & Integration

Digital Engineering

Run and assure

Cloud, DevSecOps & SRE
Quality Engineering
Assurance and governance

Policy and guardrails, control design, model risk, audit evidence

Operations and reliability

Agent operations, CI/CD as code, monitoring and SRE, incident response

We measure your delivery baseline before changing anything, so every result is a before-and-after on your own work.

Find where delivery slows
Explore the capabilities
Capabilities

What we deliver, with AI in every stage

Each service line owns stages of the lifecycle above, with AI built into how the work is done. Open one to see how we build it and where it has been proven.

Product & Platform Engineering

The right pod to take AI-built apps to production, and keep them there.

Discover, design and build

We bring a pod of senior engineers who work with AI tools every day and know where AI-generated code breaks. They add the architecture, tests, security and production environments a prototype skipped, then run the product with you on a fixed cadence.

How we build it
  • Review and refactoring of AI-generated code against architecture and security standards
  • Separate test and production environments, provisioned as code, with tenant-aware releases
  • Spec-driven development, so agents and engineers build from the same versioned specs
  • Observability and cost monitoring on every service, including LLM features
OutcomesSaaS platform on cloud releasesDashboards to decisions

Agents read the legacy code first and extract its business rules with citations to source, so migration is planned from what the system actually does. New interfaces are built for agents as well as applications.

How we build it
  • Code, schema and dependency graphs built before any conversion
  • Rule extraction with source citations, reviewed by the system owners
  • Incremental cutover, gated on rule parity between old and new
  • Event-driven integration and MCP-ready APIs with scoped, audited access
OutcomesAgents that act in live systems
AcceleratorsModernyx

Every change runs the same security and policy checks, whoever or whatever wrote it, and agents in production are monitored like any other service.

How we build it
  • Infrastructure and policy as code, versioned with the application
  • Dependency, secrets and license scanning on every commit, with a software bill of materials
  • Progressive delivery with automated rollback when the error budget burns
  • Anomaly detection and approval-gated remediation for services and agents
OutcomesFinance platform the close runs on
AcceleratorsNeuralOps

Suites are generated from the running application and kept current as it changes, with one set of agents writing tests and another judging them.

How we build it
  • Discovery of pages, flows and states without an SDK in the app
  • Self-healing selectors, where every fix is a pull request an engineer approves
  • Evaluation suites for AI features: golden sets, regression checks and drift alerts
  • A readiness score per build, enforced as a pipeline gate
OutcomesSecurity before the build
AcceleratorsQA Copilot

We baseline lead time, change failure rate and cycle time for each stage, then introduce agents where work waits longest. A named engineer reviews and owns every change the agents produce.

How we build it
  • Repositories, specs and conventions exposed to agents through MCP
  • Agent workflows for review, refactoring and test generation, each with human approval
  • Usage and cost telemetry per team and per model
  • Role-based enablement on the workflows each team nominates
OutcomesAgents certified before traffic
AcceleratorsAI Boot Camp
Outcomes and evidence

What we shipped, from the product to everything that runs it.

Where our product and delivery work shows up in the business, and the engagements behind it.

Product and R&D

AI designed into the product itself

The product improves with real usage instead of waiting for the next release.

  • AI features in the product
  • Embedded intelligence
  • Faster design and test cycles

Already shippedAn agent-led intelligence product for the media teams of a global semiconductor firm.

Engineering and IT

AI applied to your own delivery

Delivery gets faster and legacy code shrinks, using the same agents we build for partners.

  • Agentic modernization
  • Test generation and coverage
  • Reliability operations

Already shippedAgents certified above a 95% evaluation threshold before they receive any traffic.

Product & Platform Engineering
Logistics technology, multi-tenant SaaS

A growing SaaS platform, rebuilt for cloud scale and automated releases

Growth had outpaced on-premises infrastructure, releases were manual and slow, and test and production shared one environment.

What we engineered
  • Cloud migration
  • Separate test and production
  • CI/CD with tenant-based releases
  • IoT device integration
  • Mobile and partner APIs
  • Automated data imports
45%overall cost reduction by migrating from on-premises to cloud
3 hours to 1 hourrelease time through automated deployments
67%reduction in workforce dependency through automation
Product & Platform Engineering
Semiconductor, media operations

From dashboards to decisions, with the action attached

Media teams had dashboards across campaigns and regions, and no answer to what to do next.

What we engineered
  • Product engineering
  • Web experience
  • API integration
  • Cloud deployment
Every insighttraced to the campaigns behind it
City levelspend concentration surfaced automatically
Application Modernization & Integration
Software, contract lifecycle

Agents that act inside the systems teams already use

Several internal functions wanted AI on their own data, and answers alone would not change a workflow.

What we engineered
  • Write-back integration
  • Workflow triggers
  • API engineering
  • Cloud architecture
5delivery waves on one foundation
In-VPCmodel calls, with no data leaving the network
Cloud, DevSecOps & SRE
Technology, local commerce

A finance platform the close runs on

The close ran across several ERP systems, reconciled by hand, with no number traceable to its source.

What we engineered
  • Cloud platform
  • Orchestration
  • Observability
  • Release engineering
>30 to 40%faster financial close
95%+data trust across reported metrics
Partner-reported figures
Quality Engineering
Software, cloud contact center

A release path where security comes before the build

Connector requests arrived faster than they could be safely built, with no review before build.

What we engineered
  • Test automation
  • Release engineering
  • Change management
  • Forward deployed engineering
100%of connectors security-reviewed before build
~1,000daily users in the first release
AI-Native Engineering
Software, regulated reporting

Agents certified before they take traffic

Agents answered from the warehouse with nothing certifying them first.

What we engineered
  • Certification gates
  • CI for data models
  • Access architecture
  • Shared modeling standards
>95%evaluation score before an agent gets traffic
Per roledata masking on the same query
What we bring

Frameworks and accelerators for every engagement

Built and hardened before any engagement, then configured to your code and embedded in the stage where it helps most.

  • Configured to your codebasenot a generic instance
  • Runs in your cloudunder your keys, test environments first
  • Evaluated on your applicationsbefore anything touches a live system
Modernyx-logo-icon.png
Modernization
Modernize

Modernyx

Understands a legacy application before anything moves. It maps code, schema and dependencies, ranks the risk, and writes each business rule in plain English with a citation to its source.

Every extracted rule cites the code it came from
Details
qacopilot.svg
Quality engineering
Run and assure

QA Copilot

Pointed at a live application, it maps pages and flows, writes a test plan, generates and runs the scripts, and scores each build for release readiness. Fixes to broken tests wait for an engineer’s approval.

Up to 70% less pre-release validation effort
Details
neuralops.svg
Platform operations
Run and assure

NeuralOps

Watches applications and agents in production, diagnoses incidents such as process crashes, database deadlocks and full disks, and applies the fix once an engineer approves.

Every remediation approval-gated
Details
AI-Boot-Camp-logo.jpg
Enablement
All six stages

AI Boot Camp

Four role-based tracks built on the workflows each team nominates, with a baseline taken before week one and every session ending on a real task.

4 role tracks, from all staff to champions
Details
Full portfolio

Explore the accelerators

It includes the agent frameworks from our Data & AI Engineering studio, available on any engagement.

Open the portfolio
Questions we get asked

Digital engineering and AI, in detail

Something else on your mind?

Ask the team →

Product & Platform EngineeringHow do you add AI features to a product without destabilizing it?

Behind feature flags, with evaluation sets and cost and latency limits agreed before launch. Once live, each AI feature is monitored for answer quality alongside its other service metrics.

Explore Product & Platform Engineering

Application Modernization & IntegrationDo we have to freeze the old system while you modernize it?

No. Old and new run side by side, and traffic moves across in slices once business-rule parity is confirmed on real data. Rollback stays available until each slice is proven.

Explore Application Modernization & Integration

Cloud, DevSecOps & SREWill agents be allowed to change production?

Only through the pipeline, and only with approval. An agent can propose a fix or a remediation, an engineer approves it, and every action is logged and reversible.

Explore Cloud, DevSecOps & SRE

Quality EngineeringHow do you test features that depend on AI models?

With evaluation sets built from your own data, scored on accuracy, grounding and regressions on every build. A drop in the score blocks the release, just as a failing unit test would.

Explore Quality Engineering

AI-Native EngineeringHow much of your delivered code is AI-assisted, and who owns it?

Fifty to sixty percent, counted on merged and shipped code rather than on suggestions accepted in an editor. A named engineer reviews and owns every change.

Explore AI-Native Engineering
How to begin

Start where you are, decide on evidence

Each is fixed in scope and price, and ends in a decision you make on evidence. Pick the closest fit, or we will recommend one.

Let’s start a conversation.

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