AI that survives contact with the enterprise.
We build grounded agents, predictive systems and workflows aroundyour data and controls, then take responsibility for production.
People, evidence, systems
and the work Itself
Each capability has a field-realistic visual brief. The imagery shows AI inside an accountable workflow rather than representing it as an abstract object.
An operations copilot inside a real maintenance workflow
A reliability engineer in a working industrial control room reviews an AI-prepared incident summary on a normal desktop monitor. The screen connects a pump alarm, maintenance history, a cited technical manual and a proposed work order; a second operator checks the physical site camera feed.
The Model is not
the system
A useful AI product joins context, models, tools, controls and feedback into one traceable operating loop.
01 Context
Documents, databases, events and permissions
— WHERE WE START
High Value , work not AI
theatre
The best starting point is a consequential workflow with measurable friction, usable data and a clear owner.
Knowledge & service operations
Give teams fast, permission-aware answers and guided actions across fragmented enterprise knowledge.
Decision support
Combine operational data, prediction and review into repeatable decisions with an auditable trail.
Process automation
Use agents where work crosses documents, tools and approvals—but keep people in control of consequential actions.
— CAPABILITIES
From model demo to
Operating System
ScroBits works across the layers that make enterprise AI useful, observable and safe enough to depend on.
AI agents & copilots
Design and engineer grounded AI agents and copilots that use enterprise knowledge, tools and controls to complete real work.
Grounded RAG & enterprise search
Build permission-aware RAG and enterprise search systems that retrieve the right evidence and produce grounded, reviewable answers.
AI workflow automation
Combine AI, business rules, integrations and human review to automate document-heavy and decision-heavy enterprise workflows.
Predictive intelligence & optimization
Engineer forecasting, anomaly detection, risk scoring and optimization systems that connect prediction to an operating decision.
Data & AI platforms
Design data, model and application platforms that make AI systems reliable, reusable and governable across teams.
AI evaluation, observability & governance
Build evaluation, observability, guardrail and governance systems for AI applications before and after production release.
Match the Engagement to
what you know today.
You do not need a complete AI roadmap to start. You just need a worthwhile business question and a few of the people who understand it.
AI Workflow Assessment
AI Workflow Assessment
- •Pinpoint the highest-value AI use case.
- •Establish controls, architecture, and data requirements.
- •Align on success measures and pilot readiness.
Production pilot
Production pilot
- •Focus on one high-value AI use case.
- •Test it with real users and enterprise data.
- •Track agreed outcomes and validate the pilot.
Embedded AI team
Embedded AI team
- •Add AI engineering expertise.
- •Move priority AI initiatives from idea to production.
- •Build stronger enterprise AI foundations and systems.
How Our AI Services Work:
Start – Scale – Sustain
Get reliable AI outcomes faster with a staged, low risk approach that balances innovation and control.
WHAT PRODUCTION REQUIRES
Every engagement includes the unglamorous parts that make AI dependable.
— ENGINEERING RANGE
Model and Cloud-flexible by
design.
We evaluate production architecture options against quality, latency, cost, security, and operational constraints. This is our current standard stack: we do not require you to use every tool.
Models and agent systems
Retrieval and data
Product engineering
Cloud and operations
— ENTERPRISE AI QUESTIONS
Clear Answers
before the workshop.
These are the practical questions teams usually need answered before deciding how to begin.
We design, build, and deploy production-grade AI systems, including grounded RAG pipelines, autonomous copilots & agents, predictive decision intelligence, data platforms, and end-to-end evaluation & governance frameworks.
We start with a focused 2–4 week Diagnostic or Proof of Value on a high-value, measurable workflow. This aligns business objectives, establishes data readiness, and produces a validated architecture before scaling commitment.
A typical high-confidence production pilot runs between 4 to 6 weeks. It validates models with real user interactions, integrated data sources, and strict quality & latency SLAs.
Yes. Our architectures are cloud-agnostic and designed to connect securely with existing ERPs, CRMs, document repositories, vector databases, and custom microservices without requiring complete infrastructure overhauls.
We build deterministic guardrails, structured evaluation harnesses, comprehensive observability pipelines, and human-in-the-loop fallback workflows to guarantee traceability and auditability in production.
