ENTERPRISE AI · CASE STUDY
Legal Knowledge Across Connected Business Systems
Help teams find relevant information across business systems while respecting access permissions.
View case study ↗
Find answers.
Get work done.AI that helps your teams find answers and get work done.
We build AI tools that help people find company information, answer questions, and automate everyday work across your business software.
AI tools that help your team find company information and automate everyday work.
Built around your existing systems, access permissions, and approval processes.
- ENTERPRISE AI IN OPERATING REALITY
See how AI works in real business workflows
See how AI can help your team find reliable answers, review information, and complete everyday tasks, with people in control of important decisions.
1 / 5 · Scroll, swipe or tap
Legal Knowledge Across Connected Business Systems
Help teams find relevant information across business systems while respecting access permissions.
Technical details
Connects HubSpot, Gong and Google Drive, with tenant isolation, role-based access checks during retrieval, and escalation to a lawyer for review.
- WHY IT PAYS OFF
Six ways Enterprise AI can improve your work
Choose the outcomes that matter to your team, then measure them against how the work gets done today.
Speed
Decide faster
Find relevant information faster, with sources your team can review.
Automation
Skip the busywork
Automate routine steps so your team can focus on work that needs their judgement.
Service
Happier customers
Give customers more consistent answers, with human support when needed.
Savings
Spend less per task
Reduce manual effort and track the cost of completing each workflow.
Growth
Handle more work
Help your existing team manage more requests as demand grows.
Advantage
Build on your knowledge
Build AI around your business knowledge, processes, and experience.
PRODUCTION ARCHITECTUREHow we make AI work
How we make AI work
with your business
We connect your information, software, and approval processes so AI can support real work.
01 Context
Connect approved documents and systems, and check what each user is allowed to access.
- CAPABILITIES
What we can build for your teams
Start with the work you want to improve. We bring together the AI, integrations, and controls needed to support it.
01AI AGENTS & COPILOTS
Assist employees with everyday work
Help people prepare responses, complete routine steps, and use business tools with agreed permissions and approvals.
02ENTERPRISE SEARCH & RAG
Find answers in company information
Search approved documents and systems, then provide answers with source references your team can check.
03WORKFLOW AUTOMATION
Automate documents and approvals
Extract information, route work to the right people, and connect review steps to your existing software.
04PREDICTIVE INTELLIGENCE
Forecast demand and identify risks
Use business data to estimate demand, flag unusual patterns, and help teams plan their next action.
05DATA & AI PLATFORMS
Connect and prepare business data
Bring relevant data together and build the pipelines and access controls needed to support AI across teams.
06EVALUATION & GOVERNANCE
Monitor AI quality and performance
Test answers and actions, track errors and cost, and give your team a way to review and improve the system.
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.
From first conversation to
production AI, in six steps.
We agree the stages and review points with your team. Scope and timing depend on your integrations, data readiness, and approval requirements.
Discovery
Understand the problem
AI consultation & strategy
We map one workflow with your team, understand the current effort and cost, and agree what improvement would be worth pursuing.
Readiness
Check your data and existing systems
Data preparation & engineering
We review data quality, access permissions and integration options, then identify gaps that need to be resolved before a pilot.
Pilot
Build and test one useful workflow
Workflow development & evaluation
We build a focused workflow and test it with real examples and users. We compare suitable models and fine-tune only when the use case calls for it.
Integration
Connect it to everyday work
Systems integration & human review
We connect the workflow to your business tools, configure permissions and approvals, and work with users to fit it into their daily process.
Rollout
Launch with agreed checks and approvals
Deployment & integration
We review quality, security and operating checks with your team, agree the release criteria, and prepare monitoring, fallback procedures and a rollout plan.
Ongoing
Monitor and improve
Monitoring & continuous optimisation
Under the agreed support plan, we track quality, response time, cost and user feedback. We update data, prompts, workflows or models when the evidence calls for a change.
WHAT PRODUCTION REQUIRES
How we keep your AI reliable and under your control
We scope these controls around your use case and deployment. An assessment identifies what is needed; a delivery engagement implements the agreed measures.
01
Model strategy & evaluation
Compare models on real tasks and agree quality checks before release.
02
Data and retrieval architecture
Connect approved sources and enforce access permissions when information is retrieved.
03
Governance and guardrails
Define which actions need approval, when to escalate, and what records to keep.
04
Cloud and platform engineering
Plan deployment, monitoring and recovery around your infrastructure requirements.
05
Observability and optimization
Track errors, response time and operating cost so your team can spot problems and review changes.
06
Human feedback systems
Give users a way to flag mistakes and turn their feedback into reviewed improvements.
- ENGINEERING RANGE
Built to fit your technology environment
We select technology around your data requirements, quality targets, operating cost, deployment constraints, and long-term maintainability.
Explore the technologies we work with
Core Languages & Frameworks
PythonFastAPIFlaskLangChainLangGraphSemantic KernelPydanticNext.jsTypeScript
LLM Models
OpenAIClaudeHugging FaceGeminiMistralQwenLlama 3.2Amazon Titan
Agent Orchestration
CrewAIAutoGenLangGraphOpenAI Agents SDKMCPLlamaIndex
RAG & AI Patterns
Hybrid SearchRAGPrompt EngineeringKnowledge BasesFew-ShotReranking
Vector Databases & Semantic Search
PineconeMilvuspgvectorQdrantChromaFAISSWeaviateElasticsearchOpenSearchRedis Vector
Databases & Storage
PostgreSQLMySQLMongoDBRedisS3BigQuerySnowflake
Cloud & Deployment
AWSAzureGoogle CloudKubernetesDockervLLMTGIVercel
Observability & Evaluation
LangSmithLangFuseWeights & BiasesArizeOpenTelemetryGrafana
- ENTERPRISE AI QUESTIONS
Clear Answers
before the workshop.
These are the practical questions teams usually need answered before deciding how to begin.
We assess how to connect with your CRM, ERP, document storage, databases and internal apps. During scoping, we check the available APIs, permissions and any integration constraints.
We start with a workflow owner, examples of the work, and an overview of the systems involved. Together we agree the access needed, success measures, and who will help with testing, security review and adoption.
We agree the authorised users, service accounts and providers with you, then design access controls around those boundaries. Data retention and model-provider training settings are reviewed as part of the deployment plan.
For answers based on company documents, we build in source references for review. We test against real examples and agreed acceptance criteria, then define when human approval or escalation is required. We monitor errors after launch because automated checks cannot catch every mistake.
We agree maintenance responsibilities before launch. ScroBits can provide ongoing monitoring and improvements under a support agreement, or prepare a documented handover to your team. The plan defines coverage, response expectations, system owners and review responsibilities.
Ownership, repository access and handover deliverables are agreed in the project contract. We distinguish custom work from third-party tools and licences, and define what your team receives and can maintain after delivery.
We assess your cloud or on-premise requirements and document the proposed data flow. Before deployment, we agree which model providers and services process information, what they retain, and which controls your security team needs to review.
We agree a baseline before the pilot, such as time per task, resolution time, answer quality or manual effort. We compare results on agreed examples and track model usage, infrastructure and support costs so you can make an informed rollout decision.
- WHY CHOOSE US
Why build with ScroBits?
A delivery approach that connects AI engineering with your systems, your users, and clear responsibilities from pilot to handover.
AI, integrations and applications together
We build the AI workflow alongside the business integrations and application people use. Our work spans company knowledge assistants, support workflows and approved operational actions.
AI workflowsBusiness integrationsApplication engineering
Where does your team lose time?
Tell us about the information people struggle to find or the tasks they repeat. We’ll help you assess where AI could be useful.
In an initial discussion, we’ll review one workflow, the systems involved, and what success would look like.
Start the conversation