Engineering firm for technology clients. Since 2016.
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Stack landing · Python
Sevendyne Engineering Specialisations — Managed Python Pods for Global Infrastructure.
Python is our AI infrastructure stack — LLM orchestration, data pipelines, and machine learning integrations over basic web apps. We build engineering pods for analytics services, careful automation, and production ML workflows with the Transparent 15% Model and 100% IP transfer (Work for Hire).
Python is our stack for AI infrastructure: LLM orchestration, data pipelines, ML integrations, and production analytics — not throwaway scripts. We connect SaaS systems, build ETL-style jobs, and add AI-assisted workflows with guardrails, human review hooks, and cost controls.
Greenfield automation spends budget on discovery, data mapping, error handling, and monitoring — the “last mile” is where projects fail. Ongoing operations are smaller monthly increments: new rules, partner API changes, model updates, and reliability hardening.
Every Python pod ships with peer review cycles, CI/CD enforcement, and senior architectural oversight as baseline — not optional add-ons. Data and ML work needs senior judgment on idempotency, PII, and failure modes; for AI features we keep human review boundaries explicit — never auto-send without safeguards unless you explicitly accept the risk.
Deep Zoho Recruit customization plus Python/OpenAI to assist manual recruiter workflows — CSR Informatics; pre-launch programme.
Python/PostgreSQL services feeding Angular or other frontends — emphasis on reliable queries, caching where needed, and operational metrics.
Milestones are quoted after discovery — see pricing. Dedicated pods align with monthly GBP team economics under the Transparent 15% model — see role bands. Integration-heavy Python work often starts with a paid discovery milestone (fixing data mapping and risks) before larger build phases — this avoids “open-ended integration” surprises.