For your role
For Engineering Leaders
What you are trying to solve
Notebooks do not deploy themselves. Prompts in a shared doc are not version control. A vector index without refresh logic is a silent failure waiting to happen. Engineering leaders need AI work that fits CI/CD, code review, incident response and on-call — not a parallel universe of experiments.
You may also inherit legacy applications that cannot expose the APIs agents need. Modernisation and AI often belong in the same roadmap.
What we deliver for you
- Solution architecture with clear boundaries: model serving, retrieval, orchestration, tool adapters, UI
- Evaluation and regression suites in CI — prompt changes and model upgrades do not ship without passing tests
- LLMOps: deployment, monitoring, drift detection, prompt and model versioning, incident runbooks
- FinOps for inference spend — caching, routing, model selection by task complexity
- API layers over legacy systems where agents need structured access
- Infrastructure as code, documentation and pair sessions so your team can maintain what we build
How we engineer AI systems
We use the same practices as any production service: typed interfaces, automated tests, staged rollouts, feature flags and structured logging. Agents are state machines with tool calls, not unbounded chat loops.
Model selection is an engineering decision
Frontier models, open-weight models and fine-tuned models each have trade-offs in cost, latency, privacy and capability. We benchmark against your evaluation harness — not leaderboard scores — and document the decision.
Give AI an API it can trust
Agents fail when tools are ambiguous, slow or inconsistent. We design tool schemas, error responses and idempotency keys so the agent can recover from failure. Read-only tools come first; write tools follow with approval gates.
Working with your team
We can lead the AI workstream, embed alongside your engineers, or hand off after the first production release. Discovery clarifies which model fits your organisation. Weekly demos keep work visible. Nothing lives only on our laptops.
Legacy and modernisation
When core systems lack APIs, agents cannot act — only chat. Application modernisation is scoped as fixed-price per application or wave, often in parallel with or before agent increments. Discovery maps which integrations are on the critical path.
Who is awake at 3am
Production requires runbooks, on-call and rollback tested before traffic arrives. We deliver those in the build. Managed run can include incident first response; in-house ops takes full ownership with handover training if you prefer.
What we need from you
- A technical counterpart for architecture decisions and code review norms
- Access to CI/CD, staging environments and observability tools
- Clarity on security requirements: network boundaries, secrets management, allowed model providers
- Honest assessment of legacy constraints that affect API design
Questions engineering leaders ask us
Do you only work with one cloud or stack?
We build on what you run — major cloud providers, on-premise where required, air-gapped sites for OT-adjacent workloads. Discovery maps constraints before we commit to an architecture.
How do you handle prompt and model versioning?
Prompts and configs live in version control. Deployments are tagged. Evaluation suites run on every change. Rollback is a redeploy, not a scramble.
Can my team take over LLMOps after go-live?
Yes. Runbooks, monitoring dashboards and handover sessions are part of delivery. Managed run is optional if you prefer to operate in-house.
How do you structure repos and IaC for handover?
Monorepo or polyrepo follows your standards. Prompts, tool adapters, orchestration and infra templates live in git with CI gates. Nothing critical lives in a vendor portal alone.
Definition of done for engineering
Done means harness green in CI, observability dashboards live, runbook signed by ops, security review closed items resolved and internal owner named — not merely merged to main.
Related services and reading
AI Discovery and Strategy starts every engagement. Data and AI Foundations, Generative AI and Agent Engineering, ML Engineering and LLMOps, and Application Modernisation follow as scoped increments. How we work describes Perceive, Engineer, Deliver and Run. Insights publishes fortnightly on evaluation, cost, governance and operations.
Named case studies publish when clients allow naming. Until then, role pages, FAQ and Engagements describe delivery honestly — fixed discovery fee credited within sixty days, fixed-scope build increments, managed run optional, handover any time.
We do not invent client logos, outcome statistics or certifications on these pages. Select your region on contact — India, Middle East, ANZ or Other. Glossary defines terms like evaluation harness, agent, residency and increment in plain language.
Getting started
Write to hello@incubics.com with your role, region and use case in two sentences. We respond within one business day with a scoping call invite. Incubics formed in 2026; discovery is two weeks fixed fee; production by week twelve of the build. Contact form regions: India, Middle East, ANZ, Other.
Bring your sponsor, a sketch of systems in scope and honesty about active pilots. We will tell you if discovery is the right next step or if prerequisite work should come first.
Offices in Bengaluru, Pune and the USA. Legal entity: IQLEXA Technologies Private Limited, registered in Pune. Data residency by region: India, Middle East, ANZ, EU, US and other deployments as scoped.
Next step
Start with two weeks.
A fixed-fee discovery gives you a ranked use-case portfolio, a target architecture, a cost model and a build proposal you can take to your board. If we don't find a case worth building, we tell you.