Question 1

What does ScroBits actually build?

Accepted Answer

We build two kinds of production systems for enterprises:
1. Enterprise AI: grounded agents, predictive models and decision workflows wired into your data, controls and operating reality. The model is not the system; we build the system around it.
2. Physical AI: camera, sensor and map platforms that turn the physical world into operations, including fleet and driver assistance, site/change detection, machine health, and inspection workflows.
We don't do staff augmentation, generic app shops, or chatbots-for-the-sake-of-it.

Question 2

How do you engage: fixed scope, retainer, or product co-build?

Accepted Answer

Three models, picked after a scoping call: 1. Scoped delivery: a defined system with milestones and a fixed envelope. Best for a first production use-case. 2. Program retainer: a dedicated squad embedded with your team, operating on quarterly outcomes rather than tickets. 3. Co-build / platform partnership: longer-horizon work where ScroBits owns an operational capability (for example, the Physical AI platform behind your fleet operations). Pricing is per engagement, not per hour. We share a budget envelope after we understand scope.

Question 3

How do you handle data security, IP and compliance?

Accepted Answer

Four defaults on every engagement: 1. Mutual NDA before any brief is shared. 2. Your data stays in your tenancy wherever possible, whether on-prem, in your cloud account, or on ScroBits-managed infra with explicit boundaries. 3. Role-based access, encryption in transit and at rest, auditable deployment and change controls. 4. IP ownership on delivered systems transfers to you; our reusable platform components are licensed. For regulated environments (defense, utilities, healthcare, financial services) we support air-gapped, hybrid and private-cloud deployments.

Question 4

How quickly can we go from first conversation to a live pilot?

Accepted Answer

Typical cadence: 1. Scoping call: 1 week from inquiry. 2. Written brief and approach: 1 to 2 weeks after that, including the shape of the system, data we'll need, and success criteria. 3. Pilot in a bounded environment: 4 to 8 weeks from kick-off for Enterprise AI; 6 to 12 weeks for Physical AI pilots that involve on-site hardware or edge deployment. 4. Production rollout: phased, with a dedicated operator per site, line, or fleet.

Question 5

Who owns the system once it's live, you or us?

Accepted Answer

You own it. Code, models, data and operational runbooks live in your repositories and your tenancy. We hand over architecture docs, training/evaluation pipelines, and the dashboards your team will use day-to-day. Where you want us to continue running the system under SLA, we offer a managed-operations option; where you want to take it in-house, we transition it to your team with structured enablement.

Question 6

What stack and infrastructure do you work with?

Accepted Answer

We're pragmatic. The system shapes the stack, not the other way around. Common choices on recent engagements: 1. AI/ML: PyTorch, HuggingFace, RAG with vector stores (Qdrant, pgvector), LLM providers (Anthropic, OpenAI, open-source models), eval and tracing (LangSmith, custom). 2. Physical AI / edge: computer vision (YOLO family, custom detectors), ONNX/TensorRT on NVIDIA Jetson, MQTT/OPC-UA for sensor data, offline-first edge agents. 3. Platform: TypeScript/Python services, Kubernetes, AWS/GCP/Azure or on-prem, Postgres, event streaming (Kafka/Redis Streams), Next.js dashboards. Legacy integration (SCADA, ERP, MES, warehouse systems) is a first-class concern, not an afterthought.

Question 7

Do you work with teams outside India?

Accepted Answer

Yes. Our team is based in Pune (HQ), with active engagements across North America, Europe, the Middle East and APAC. We cover US and EU business hours with overlap on our side, and we travel to client sites for Physical AI deployments.

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