Engineering capacity for remote-first organisations, with the EU AI Act in view.
Remote-first organisations buy engineering on evidence: does the vendor work inside our tooling, does the AI feature actually work, and can we prove that to a customer or regulator anywhere we sell. The EU AI Act's high-risk obligations, in force from August 2026, make that last question unavoidable.
Noida delivery base
- Remote-first product companiesWith distributed teams and asynchronous habits, who need a partner that works inside their repositories, review flow and documentation rather than beside them.
- Multinationals and platforms selling into the EUWhere an AI feature used for hiring, credit, education or essential services falls into the high-risk category and needs provider or deployer obligations met.
- Companies whose customers ask for ISO 42001 or NIST evidenceWhere enterprise procurement now expects an AI management system and audit evidence, and a demo no longer closes the deal.
- Teams that need to scale engineering without adding headcountDedicated senior engineers or a fixed-scope build, with a hand-over plan so the capability stays with the client.
What we see holding AI programmes back in this market, from the engagements we run and the regulation now in force.
- 01
High-risk obligations under the EU AI Act apply from 2 August 2026
Providers must have a documented risk management system, data governance, technical documentation, logging and human oversight. Deployers must use systems as instructed, keep logs for at least six months, monitor operation and report incidents. A proposed delay has not been enacted.
- 02
Deployer duties are specific and technical
Article 26 requires competent human oversight, control over input data, automatic log retention and worker notification where systems are used in the workplace. Each is a property the system either has or does not.
- 03
ISO 42001 is moving from differentiator to table stakes
Procurement teams in finance, healthcare and EU jurisdictions increasingly require evidence of a formal AI management system, and know how to read an ISO certificate better than a self-attestation.
- 04
Most AI projects fail before they reach production
Independent studies put AI project failure well above 80 percent, and most generative AI pilots deliver no measurable return. The common causes are the same everywhere: pilots run in sandboxes disconnected from production data, single-vendor architectures that lock the company in, and no governance in place when the board asks for approval.
- 05
Hallucination is an operational cost, not a research topic
Wrong answers from language models are mostly caused by retrieval of inconsistent or undocumented production data, and they cost real money in rework, complaints and lost trust. Evaluation and observability for the AI layer lag traditional software operations by years in most organisations.
- 06
Remote vendors are judged on security controls first
SOC 2 Type II, ISO 27001, GDPR alignment, MFA, role-based access and named breach-response commitments are baseline expectations of an engineering partner in 2026, not premium features.
What each obligation means for the system itself. We build the controls; your counsel confirms the legal position.
Security review inside the build, not after it
Threat model, access control, secrets handling, dependency scanning and audit logging are designed with the architecture and documented as evidence your own auditor can read. Nothing is retrofitted for a questionnaire.
Controls documented in the form an ISO 27001 or SOC 2 auditor expects, in whichever jurisdiction the audit happens.
AI audit and evaluation you can hand to a regulator
Every AI feature ships with an evaluation set, grounding and hallucination measurements, regression runs per release and traced inputs and outputs. The result is an evidence pack, not a demo.
Evaluation sets, regression runs and traces that serve as Article 26 logs and as ISO 42001 evidence.
Compliance mapped to the build
We map the system to the obligations that apply to it, control by control, so legal and security teams review a document rather than reverse-engineer the code. We are engineers, not your counsel, and we say so.
EU AI Act classification and obligations mapped to the system's documentation, oversight and records.
Data residency and model-provider exposure settled in architecture
Where data lives, which model providers see which fields, and what stays in-region are decisions made in the architecture phase with options costed, not discovered in procurement.
Regional hosting per market and a documented map of every call that crosses a border, including model providers.
Human oversight for consequential decisions
Approval steps, confidence thresholds and human review queues are built into workflows that touch money, eligibility, health or employment, with logs that show who approved what.
Human oversight designed to the standard the Act requires for high-risk decisions, with logs to prove it.
Portability and cost control by design
Model-portable interfaces, cost budgets per feature and monitoring from day one, so a provider change or a price change is a configuration decision rather than a rebuild.
Model- and cloud-portable design, so regional provider constraints or price changes are configuration decisions.
Only the services and AI solutions we run in production are listed. Anything outside this catalogue is not something we sell.
- Cloud & DevOpsWe make releases safer, infrastructure more observable, and product systems ready to scale.
- Custom Software DevelopmentWe design and build secure, scalable software around the way your business actually operates.
- Data EngineeringWe connect scattered business data into reliable pipelines, dashboards, and decision-ready intelligence.
- Resource AugmentationAdd capable engineers, designers, and AI specialists who work with your team, tools, and delivery rhythm.
- Agentic AIBuild AI agents that route work, coordinate tools, review information, and assist operational teams.
- LLM ApplicationsEmbed large language models into products with reliable UX, data boundaries, and production monitoring.
- MLOpsDeploy, monitor, evaluate, and improve AI models after launch.
- Predictive AnalyticsTurn historical and operational data into forecasts, risk signals, and decision-ready analytics.
- RAG SystemsCreate retrieval-augmented search and answer systems over private documents and business knowledge.
Sectors we serve hereEducation · FinTech · Healthcare · Logistics · SaaS
- Inside your tooling, asynchronouslyWe work in your repositories, review flow, issue tracker and documentation. Decisions are written down, so time zones stop being a dependency.
- Overlap where it countsOur day overlaps the European afternoon and the US morning. Launch windows and incident cover are planned in advance for any region.
- Evidence as a deliverableEvery AI feature ships with its evaluation results, architecture record and control documentation, so your sales and compliance teams answer procurement from the same source.
- Capability that stays with youDedicated engineers or a fixed-scope build, with documentation and hand-over planned from the start so the knowledge does not leave with the vendor.
Yes. EU and UK regions of the major clouds are standard, and we document any call that leaves, including to model providers, with the transfer mechanism that covers it.
Building for a global team? Let's take it to production.
Share your product, AI, workflow, or data challenge. We will help identify the right starting point, delivery risks, scope, timeline, and next step.
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