Supply chain security in every pipeline
Dependency, license and secret checks run on every build, with evidence ready for SOC 2 and customer reviews.
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Technology & Software
Engineering from product vision to a scaled platform
Product engineering, platform modernization and quality engineering for software and technology companies, with AI features and agents tested for accuracy, security and scale so you can ship faster without trading away reliability or customer trust.
Agentic AIbuilt into products and delivery
LLM evaluationfor accuracy, safety and drift
SOC 2control testing and audit evidence
What’s changing across the industry
The pressures on technology and software companies
Every organization we work with is balancing the same pressures.
AI in every roadmap
Customers now expect AI features in the product, and those features have to be accurate, safe and affordable to run.
Faster releases, same quality bar
Teams ship weekly or daily, so regression, performance and security checks have to keep pace with every change.
Security evidence before the sale
Enterprise buyers ask for SOC 2 reports, penetration test results and secure development practices before they sign.
New rules for software and AI
The EU Cyber Resilience Act and the EU AI Act set new expectations for vulnerability handling, transparency and AI governance.
How we help
Serving the full technology value chain
We understand the challenges in each part of the technology sector and help through software-specific offerings, in addition to the domain-agnostic services we deliver across the lifecycle.
Subscription software
SaaS & platform companies
Subscription software and platform businesses shipping to many customers from one codebase.
What we hear
- Release speed that outpaces regression testing
- Tenant isolation, uptime and performance at scale
- Security questionnaires slowing enterprise deals
How we help, across the lifecycle
- Advise
- Architecture and platform assessmentsAI-assisted analysis of code, dependencies and architecture to find hotspots
- Build
- SaaS product and feature engineeringAI pair-programming with engineers owning every release
- Multi-tenant platform engineeringAI-assisted review of tenant isolation and configuration
- Modernize
- Monolith to microservices refactoringAI-assisted code analysis to find service boundaries and plan the split
- Verify
- Functional and regression test automationAI-generated and self-healing test scripts, reviewed by QA engineers
- Performance, load and scalability testingAI-modeled load profiles based on production usage
- SOC 2 and ISO 27001 control testingAI-assisted collection and mapping of evidence to controls, reviewed by your compliance team
- Run
- 24x7 monitoring and site reliability engineeringAI detects anomalies before customers notice them
Proof point
A workplace operations start-up reached a one-service marketplace pilot in about 10 weeks.
See the case studySoftware vendors
Enterprise software vendors
Independent software vendors selling complex products into large enterprises.
What we hear
- Legacy code and frameworks that slow every release
- Customers asking for accessibility and security evidence
- Integration and upgrade testing across many customer setups
How we help, across the lifecycle
- Advise
- Quality engineering maturity assessmentsAI analysis of defect, test and release history to find coverage gaps
- Build
- APIs, SDKs and integration platformsAI-generated API code, client SDKs and documentation, reviewed by engineers
- Modernize
- Framework and language upgradesAI agents draft upgrade changes and fixes, with engineers approving every merge
- Verify
- API and contract testingAI-generated API tests from specifications and real traffic patterns
- Accessibility testing against WCAGAI-assisted scans, with manual testing using assistive technology
- SOC 2 and ISO 27001 control testingAI-assisted collection and mapping of evidence to controls, reviewed by your compliance team
- Run
- Release engineering and production supportAI-assisted release risk scoring and rollback recommendations
AI products
AI-native product companies
Companies whose core product is built on models, LLMs and AI agents.
What we hear
- Model accuracy, hallucination and safety in production
- Inference costs that grow faster than revenue
- AI governance questions from customers and regulators
How we help, across the lifecycle
- Advise
- AI use-case prioritization for products and operationsAI models score each use case on customer value, cost to run and risk
- Responsible AI and governance frameworksPolicies drafted with AI support and mapped to the EU AI Act and NIST AI RMF by our consultants
- Build
- AI features and agentic workflows in productsAI agents designed with guardrails, tool limits and human approval steps
- Retrieval-augmented generation and LLM applicationsAI-assisted chunking, retrieval tuning and prompt evaluation, reviewed by engineers
- Modernize
- Cloud cost optimizationAI analysis of usage patterns to recommend rightsizing and scheduling
- Verify
- LLM and AI agent evaluation for accuracy, bias and safetyAI-generated adversarial prompts and evaluation sets, scored against agreed thresholds
- Run
- LLM and AI agent observabilityAgents and models monitored for accuracy, drift, cost and policy breaches
Infrastructure
Data & cloud infrastructure providers
Data platform, cloud, infrastructure and developer tools companies.
What we hear
- Reliability expectations close to 100% uptime
- Software supply chain and vulnerability handling
- Cloud costs that squeeze margins
How we help, across the lifecycle
- Advise
- Data and AI readiness assessmentsAI-assisted profiling of data quality, coverage and lineage
- Cloud cost and build-versus-buy reviewsAI analysis of usage and billing data to model cost per tenant and feature
- Build
- Data platforms and pipelinesAI-assisted pipeline code, schema mapping and data quality rules
- Developer platforms and internal toolingAI coding assistants and agents built into internal developer workflows
- Modernize
- Cloud migration and multi-cloud platform engineeringAI-generated infrastructure as code, reviewed by platform engineers
- DevSecOps pipelines and software supply chain securityAI checks on code, dependencies, SBOMs and infrastructure in every pipeline
- Cloud cost optimizationAI analysis of usage patterns to recommend rightsizing and scheduling
- Verify
- Chaos engineering and resilience testingAI-selected failure scenarios based on architecture and incident history
- Run
- 24x7 monitoring and site reliability engineeringAI detects anomalies before customers notice them
IT services
IT & digital services firms
IT services, consulting and digital agencies delivering software for their own clients.
What we hear
- Pressure to add AI to delivery without losing quality
- Client demands for test coverage and release evidence
- Specialist skills needed for short, intense programs
How we help, across the lifecycle
- Advise
- Quality engineering maturity assessmentsAI analysis of defect, test and release history to find coverage gaps
- Build
- Client application and platform engineeringAI pair-programming and code review, with engineers owning every client release
- Modernize
- Data platform modernizationAI-assisted data profiling, cleansing and lineage
- Verify
- Functional and regression test automationAI-generated and self-healing test scripts, reviewed by QA engineers
- Security and penetration testingAI agents probe APIs and applications for vulnerabilities, with findings confirmed by testers
- Run
- Incident response and on-call supportAIOps that correlates alerts and suggests the fix
- Managed cloud and platform operationsAI triage of tickets and automated fixes for known issues
Delivery principles
How we engineer for technology and software companies
Contract tests between every service
APIs and events are tested at their boundaries, so teams can release independently.
Tested at your largest customer’s scale
Performance, tenant isolation and cost are tested at the load your biggest accounts will bring.
AI features evaluated for quality, cost and safety
Every model and agent has evaluations that run before release and keep running after it.
Ship behind flags, roll back in minutes
Small releases behind feature flags keep the roadmap moving without risky big-bang launches.
Release frequency and escaped defects as shared metrics
Engineering and product agree on the numbers that show delivery is getting better.
Case study
Application Engineering | Workplace operations start-up
Marketplace development leveraging AI
~10 wksto a one-service pilot
6priority service categories in the first group
20+trade categories at full scale
Challenge
Facilities teams at multi-site enterprises had no reliable way to hire trade service providers. Search, referrals and directories were unscreened and unaccountable. The start-up faced a two-sided cold start, liability risk from poorly vetted providers, and enterprise expectations of SOC 2-grade security.
What we did
We started with a phased MVP: one service and curated, ops-assisted onboarding, then grew it into a full marketplace. We developed buyer, vendor and admin portals covering RFPs, contracts, projects, compliance documents and payments. RFP scoping and vendor matching were AI-assisted, with human review, on a cloud-native, SOC 2-ready AWS platform.
getting started
Start small, prove value, then scale
We recommend starting with an understanding of the current state and then expanding across the lifecycle.
- Start here
Assessment
2 to 4 weeks Pilot
6 to 10 weeksManaged team
3 to 6 monthsExpansion & transformation
Ongoing
Schedule a discovery call
A 60-minute call to scope your assessment. We bring an initial view of quick wins for your landscape.
Let’s build software people can trust
Talk to our technology engineers about the product, platform or AI feature you want to build, modernize or verify next.