Uses historical and real-time data to forecast outcomes. Examples: inventory demand forecasting, fraud detection scoring, credit risk assessment, workforce capacity planning.

AI-powered interfaces that respond to natural language. Examples: internal knowledge assistants, customer support bots, HR policy Q&A, procurement request handling.

AI that creates new documents at scale. Examples: automated report generation, loan application summaries, contract drafting, compliance document review.

AI that surfaces hidden patterns. Examples: customer churn signals, pipeline health scoring, document classification, sentiment analysis on support tickets.

A structured analysis of one business process to identify the highest-value AI automation entry point. Delivered as a scoped report with a concrete, costed implementation pathway.

• Process map annotated with bottleneck markers
• Automation score per process step (effort vs. impact)
• Top use case with rough ROI estimate
• Implementation roadmap phased into 3 stages

Think of it as giving the AI a private search engine over your company's documents. When someone asks a question, the AI searches first, finds the right policy or procedure, then answers — quoting the source. It never makes up an answer that isn't in your documents.

• Confluence / Notion — internal wikis and runbooks
• SharePoint / Google Drive — file storage and policies
• PDF / Word documents — procedures and compliance docs
• Slack history — surfacing institutional knowledge

Repetitive document processing: extracting structured data from forms, classifying incoming document types, generating outputs from unstructured text, routing documents to the correct downstream workflow.

When AI confidence falls below the configured threshold, the document is flagged for human review rather than processed automatically. The system never silently makes a high-stakes mistake.

The AI classifies each message by intent and confidence score. High-confidence intents are handled automatically; ambiguous ones escalate to a human agent with full conversation context pre-populated.

• Reads full customer history before composing a reply
• Logs every AI interaction to the CRM record
• Creates tickets or updates case records automatically
• Escalates with conversation context pre-loaded

Hours of manual data gathering, spreadsheet consolidation, and report drafting. The AI connects directly to data sources, queries them on schedule, summarizes findings in your preferred format, and delivers the report automatically.

• SQL databases — direct query via read-only credentials
• BI tools — Tableau, Power BI, Looker exports
• APIs — internal and third-party data feeds
• Spreadsheets — Google Sheets, Excel via scheduled sync

Recommendations scoped to the client's actual tech stack, team capabilities, and data readiness. Output is a prioritised roadmap with effort/impact scoring — not a slide deck full of buzzwords.

• Data readiness — quality, availability, governance
• Integration feasibility — existing systems audit
• Team capability — skill gaps and training needs
• ROI modelling — time and cost savings per use case

Deployed within the client's own AWS, GCP, or Azure account. Data never leaves their cloud environment. DeView manages the deployment — not the data.

Fully air-gapped within the client's physical or virtual infrastructure. Uses open-source models (Llama 3, Mistral) running locally. Required for highly regulated environments.

Sensitive regulated data stays on-premises; non-sensitive processing uses cloud inference. Common in lending where some datasets are regulated and others aren't.

• Role-based permissions — users see only what they need
• Service accounts — systems talk with scoped keys only
• Row-level security enforced at database layer
• Admin access server-side only, never client-side

• Every AI action logged — who, when, what input, what output
• Immutable records — logs cannot be retroactively altered
• Export available — full audit trail for compliance review
• Timestamps — created_at / updated_at on all records

• Server-side validation — all input sanitised before processing
• Length limits — prevent injection and overflow attempts
• Honeypot fields — bot and spam detection on all endpoints
• Domain validation — rejects personal emails for B2B flows

• Prompt injection mitigation — input sanitised before LLM
• Output filtering — AI responses screened before delivery
• Domain guardrails — model constrained to defined scope
• Confidence thresholds — low-confidence outputs escalate to humans

• Response latency — is the AI responding within SLA?
• Error rates — are requests failing or timing out?
• Input distribution — are query types shifting?
• Output consistency — are answers staying on-topic?

• Accuracy — are answers factually correct?
• Relevance — are answers appropriate for each query?
• Drift score — how far has performance shifted from baseline?
• Human feedback — thumbs up/down signals from end users

Incoming data starts looking different from the training data. Detected by comparing statistical distributions of inputs over time.

The relationship between input and correct output changes. Detected by declining accuracy scores on sampled outputs.

• Project title and client name from Supabase
• Current stage indicator — which phase the engagement is in
• Milestone timeline — done / active / upcoming with animations
• Progress bar — currentStage / total milestones

• Reference code acts as access token — no password required
• Server-side queries only — Supabase key never in browser
• is_active flag — any portal disabled instantly
• Fallback resilience — Task Manager API if Supabase unavailable