Turn one high-value workflow into a working AI system.

Connect AI to the tools and data your team already uses, with grounded outputs, review paths, and operational visibility built in.

Scope an AI workflow pilotSee what the engagement includes
Best for
Manual document, knowledge, routing, or support workflows
First engagement
One workflow inside an existing system
Typical pilot
2-4 weeks
Decision
Harden, expand, revise, or stop with evidence

This is likely the right starting point when

  • People copy and paste between systems to complete routine work
  • Documents or requests wait on manual review and routing
  • Teams need answers from private sources with citations
  • Current tools lack quality visibility or a clear review path
Deliverables

What we deliver

  1. 01

    Working AI workflow

    A usable pilot integrated with the system where the work already happens.

  2. 02

    Grounded data connection

    Authorized sources, retrieval logic, citations, and explicit data boundaries.

  3. 03

    Guardrails and review

    Quality checks, exception handling, human review, and audit visibility.

  4. 04

    Operating handoff

    Evaluation criteria, runbooks, architecture decisions, and a scale recommendation.

Example workflows

Integration Gallery

01

Support Copilot

Zendesk · Intercom · Salesforce · Custom portals

Add AI-powered chat to your existing customer portal or internal helpdesk. Answers from your knowledge base, escalates to humans when needed.

Outputs: Instant answers, Ticket routing, Escalation triggers

Review queue for low-confidence responses

02

Document Automation

SharePoint · Google Drive · ERP systems · Approval workflows

Extract key fields from invoices, contracts, and forms. Route to the right approver, flag exceptions, and keep your systems in sync.

Outputs: Extracted data, Routing decisions, Exception flags

Exception review for edge cases

03

Private Search (RAG)

Confluence · Notion · SharePoint · S3/databases

Answer questions using your internal docs; policies, SOPs, product manuals; with citations and confidence scores.

Outputs: Grounded answers, Source citations, Confidence scores

Feedback loop for answer quality

Implementation

How a grounded AI workflow fits together

An example private-search flow showing retrieval, model, review, and observability components. The exact architecture follows the selected workflow and security model.

RetrievalGenerationcheckhitsampletune
Query
Cache
Embed
Vector DB
Keyword
Rerank
Prompt
LLM
Guardrails
Eval

Key Decisions

  • Chunking strategy: Semantic vs. fixed-size depends on document structure and query patterns.
  • Embedding model: OpenAI, Cohere, or open-source based on cost/latency/privacy tradeoffs.
  • Search quality (for technical teams): We pair keyword + vector search and rerank results; we track quality with light checks.

Integration Points

  • Data sources: SharePoint, Confluence, S3, databases via scheduled or event-driven sync.
  • LLM provider: Works with major AI providers or your private models; we keep you flexible.
  • Guardrails: Safety checks like content filtering and redaction before responses go out.
  • Observability: Monitoring and quality checks so you can see cost, speed, and accuracy.
Focused engagement

From one workflow to a production AI system

  1. 01

    Select

    Define the workflow owner, current baseline, sources, and measurable outcome.

  2. 02

    Design

    Agree on integrations, evaluation criteria, guardrails, and review paths.

  3. 03

    Ship

    Deliver a working pilot inside the target workflow.

  4. 04

    Decide

    Harden, expand, revise, or stop based on quality and usage evidence.

Production controls

Guardrails & Safety Path

  1. 01

    Input sanitization

    Clean and validate user input before processing.

  2. 02

    PII redaction

    Detect and mask sensitive data in inputs and outputs.

  3. 03

    Content policy filter

    Block harmful, inappropriate, or off-topic requests.

  4. 04

    Retrieval validation

    Verify source documents are relevant and authorized.

  5. 05

    Response constraints

    Enforce tone, length, and factuality requirements.

  6. 06

    Human review queue

    Route low-confidence responses for manual approval.

  7. 07

    Audit logging

    Record all interactions for compliance and debugging.

Scope

What the scope can include

  • Customer support inside the tools your team already uses, giving customers faster answers and staff better assistance.
  • Documents handled automatically; pull key details from PDFs/emails, route them, and redact sensitive info without changing your workflow.
  • RAG pipelines (private knowledge search); answer using your docs and data (policies, SOPs, invoices) so responses stay accurate.
  • Smarter handoffs & follow-ups; auto-triage requests, send approvals to the right person, and escalate edge cases.
  • Automation across your existing systems; connect AI to CRM/ERP/HR tools to reduce manual work and keep records up to date.
  • Safety + visibility; add guardrails, quality checks, and monitoring so you can trust the output and see what’s working.

A clear division of responsibility

Semper AI brings

  • Solution lead and architecture
  • Hands-on implementation
  • Quality, security, and observability
  • Decision log, runbooks, and handoff

Your team brings

  • A workflow owner
  • Access to relevant systems and data
  • People who can make scope decisions
  • Time to review the working pilot
FAQ

Questions before you start

01Do we need to replace our current systems?

Usually not. The first goal is to redesign one workflow using the systems, permissions, and data your team already has.

02How do you handle safety and brand voice?

We define retrieval rules, response constraints, evaluation checks, and human review based on the selected workflow and risk level.

03Can you deploy privately?

Yes. We can work inside your cloud, use private endpoints, and design around your access and data-residency requirements.

04How fast is a pilot?

A focused pilot is usually scoped to 2-4 weeks. Integration access, source quality, and review availability determine the final schedule.

Start with the workflow creating the most manual friction.

We’ll map the work, data, integrations, and review path, then recommend the smallest useful pilot.

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