Service Practice · Technology Ops

Engineering teams, run as one capability.

Most firms buy engineering through five different vendors and try to integrate the seams themselves. We exist because the seams are where the work fails. Technology Ops is built for product organisations that need software, AI, quality, infrastructure, and support held to one standard, by one accountable team.

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Technology OpsData OpsBusiness IntelligenceFinance
What This Practice Covers

One capability, five disciplines.

Technology Ops covers the engineering disciplines we run for clients: software engineering, AI engineering, quality engineering, DevSecOps, and IT and application support. Each is a real discipline with its own depth. What we do that most firms do not is run them as one team, with one Practice Lead accountable for the whole. The result is fewer integration costs, faster decisions across disciplines, and a single point of accountability for the senior reader who has to answer for engineering performance.

Software & Application Engineering

Senior engineers working as an extension of your product team. Full-cycle delivery from definition through release and iteration, against your engineering cadence and standards.

  • Tech stack: Python (Flask, SQLAlchemy, FastAPI), Node.js, PHP on the backend. React, Vue, modern JavaScript on the frontend. Databases across Postgres, MySQL, Mongo, and Redis. Cloud and CI on AWS, Jenkins, GitHub Actions, and Docker.
  • Engagement shapes: Embedded engineering capacity, lead engineer roles for offshore teams, feature delivery against an existing roadmap, intern pipelines for sustained team growth.

AI Engineering

LLM-powered application development, agentic systems, and the production discipline that LLM-driven systems require.

  • What we build: Multi-step reasoning systems with tool use and autonomous decision-making, retrieval and RAG architectures, real-time AI-driven communication features, and the security, safety, latency, and cost controls production AI needs.
  • Tech stack: LangChain, LlamaIndex, and similar orchestration frameworks. LLM APIs from OpenAI, Anthropic, Google, and other providers. Python and JavaScript / TypeScript on the backend. FastAPI, Express, and async event-driven architectures.

Quality Engineering

The operating discipline behind every credible release. Testing strategy, automation architecture, performance engineering, and SDET-led automation-first transformations.

  • Coverage areas: Functional, automation, mobile, API, and specialised testing. Performance and load testing. Platform-specialist testing on Salesforce, Workday, ServiceNow, and Oracle EBS.
  • Tooling: Cypress, Playwright, and Selenium at the UI layer. JMeter and Postman at the API layer. Maven, Jenkins, and GitHub Actions for CI/CD integration. Splunk and ELK for workload modelling, Dynatrace, New Relic, and Prometheus for performance profiling.
  • Engagement shapes: Embedded QE function across product squads, automation-first transformation programmes, dedicated performance engineering, platform-specialist coverage.
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QA-Path

AI-Powered Test Automation, eliminate manual effort, catch bugs earlier, and deliver release-ready confidence at every sprint.

QA-Path

DevSecOps & Infrastructure

Infrastructure-as-code, CI/CD reliability, cloud-native security, and the observability stack that keeps production trustworthy.

  • Tech stack: Terraform for IaC. AWS surface including Route 53, ELB, API Gateway, EC2, ECS, EKS, RDS, and S3. Observability across CloudWatch, CloudTrail, Grafana, Prometheus, Splunk, DataDog, and PagerDuty.
  • Engagement shapes: Senior DevOps capacity, IaC migration and modernisation, observability platform build-out, on-call and incident management.

IT & Application Support

End-user IT support across global office footprints, plus L1 / L2 application support for production environments. Structured triage, root-cause analysis, and a feedback loop into engineering.

  • Coverage areas: macOS and Windows fleets. Identity and device management on Okta, Microsoft 365, JAMF Pro, and Intune. Web conferencing support (Zoom, Microsoft Teams). End-user knowledge base authorship and infrastructure documentation.
  • Application support coverage: Python, PHP, and Node.js production surfaces. AWS service-level investigation. JIRA-based ticket management. Pattern detection across recurring failures and feedback to product and engineering for permanent fixes.
Strategic Counsel

Before we deliver, we think.

The Consulting Practice partners with senior leaders on the strategic questions that sit above the disciplines: how the engineering function should be structured, where AI fits relative to product engineering, how IT and DevOps coordinate without overlap, and which tooling decisions are worth the cost of switching.

Engineering Operating Model Design

Structure, role definitions, accountability lines, and the operating cadence that makes a technology function durable. Including the perennially difficult questions: how engineering and quality share accountability; how AI sits relative to product engineering; how IT and DevOps coordinate.

Capability Diagnostics

Benchmarks of your current capability across one or more sub-disciplines: coverage, maturity, tooling alignment, talent depth. Resolved into a phased roadmap.

Tooling, Platform, and Vendor Strategy

Independent guidance on engineering tooling, AI tooling, observability platforms, and the build-versus-buy decisions that shape the technology stack. We do not resell software. We do not take vendor referral fees.

AI Readiness Assessments

Honest evaluation of where AI can move the needle in your product and operations, where it cannot, and what the engineering and data prerequisites are for production deployment.

Outcomes

Outcomes our clients see.

Engineering velocity sustained without incident-rate increase

Test automation coverage moved from below 20% to above 75% on critical paths

AI-powered features moved from prototype to production with appropriate guardrails

L1 / L2 support function reducing engineering escalations by 50% or more

Operating cost of the engineering function reduced while output expanded

Case Studies

The work, in practice.

Four Technology Ops engagements span the range of what the practice delivers.

Quality Engineering for a Document AI Platform

Multi-year managed engagement covering the full quality and reliability surface for a SaaS platform serving the world's largest alternative investors.

See case study

Test Automation Transformation for a US Fintech

SDET-led automation-first transformation for a US-headquartered international fintech, including CI/CD-integrated automation and an offshore QA team rebuild.

See case study

Embedded Engineering for a US Public Asset Manager

Senior full-stack and AI engineering capacity operating as an extension of a US-listed asset manager's product organisation.

See case study

Database, DevOps & IT Operations for a Credit Intelligence Firm

Senior DBA, DevSecOps, and IT support function running the operational backbone of a global credit intelligence platform.

See case study
Pairs Well With

Where this practice connects.

Data Ops

When the engineering work touches the data layer, or when AI feature work requires the data infrastructure to be ready for it.

Business Intelligence

When the engineering surface includes customer-facing analytics or embedded reporting.

Consulting Practice

When an engineering operating model decision precedes the delivery work.

Talk to a Technology Ops Practice Lead.

Speak with a Technology Ops Practice Lead