AIMass

Practical AI Implementation - RAG & Automation

AI-powered search interface showing accurate results with citations
Source CitationsHuman Oversight
Enterprise Safe
Overview

Deploy AI that drives real business metrics, not just hype

Cut through AI hype with targeted implementations that solve real problems. We build RAG search systems, support copilots, and workflow automation with proper safeguards, citations, and measurable impact on your key business metrics.

  • Implementation Time
    6–10 weeks typical
  • Specialization Areas
    RAG search, copilots, automation
  • Safety Standards
    PII protection & abuse prevention
  • Best for
    Companies with existing data and workflows
Technologies
Retrieval Augmented GenerationVector DatabasesLLMsEvaluation FrameworksMonitoring
Focus
RAG SystemsLLM IntegrationSafety GuardrailsPerformance Evaluation
Get a scope

We solve the right problems

When users stall or churn, we design a path to value they can’t miss.

  • AI hallucinations undermine trust

    Source citations and evaluation frameworks ensure responses are grounded in your actual data, not AI imagination.

  • No clear connection to business value

    We focus on measurable outcomes: customer deflection, time savings, or user activation rather than cool AI demos.

  • Privacy and safety concerns block adoption

    Built-in PII redaction, abuse filters, and human oversight make AI safe for production environments.

Our approach

Business-first AI

Every AI implementation must solve a real business problem with measurable impact, not just demonstrate cool technology.

Safety by design

Privacy protection, abuse prevention, and human oversight are built into every AI system from the beginning.

Transparent performance

Clear evaluation metrics and monitoring help you understand when AI is working well and when it needs improvement.

Where this wins

Patterns we’ve shipped across industries.

  • Intelligent document search with citations

    Help users find accurate information in your knowledge base with source citations and confidence scores.

  • Customer support response drafting

    Generate contextual support responses with human review workflows and escalation paths for complex issues.

  • Personalized user onboarding guidance

    Provide tailored setup assistance and feature recommendations based on user goals and usage patterns.

Behind the design

Design that ships cleanly to dev

We don’t stop at pretty. We deliver tokens, states, and specs developers can implement fast—without guesswork.

  • 98%

    Client satisfaction

  • 10

    Weeks to MVP avg.

Our process

  • 1
    Success metrics and use case definition
    • Identify measurable business impact target
    • Map existing data sources and quality
    • Define success criteria and failure modes
  • 2
    Rapid prototype development
    • Build small-scale evaluation dataset
    • Set latency and cost budgets
    • Create initial user experience proof-of-concept
  • 3
    Safety and compliance implementation
    • PII detection and redaction systems
    • Abuse prevention and rate limiting
    • Human review and escalation workflows
  • 4
    Production system development
    • Data processing and retrieval pipelines
    • Response generation and quality scoring
    • User interface design and integration
  • 5
    Evaluation and quality assurance
    • Offline evaluation suite development
    • Human feedback collection systems
    • Continuous quality monitoring setup
  • 6
    Controlled production launch
    • Limited user group rollout strategy
    • Real-time performance monitoring
    • Feedback collection and iteration planning
  • 7
    Impact measurement and optimization
    • Baseline comparison and impact analysis
    • Scaling strategy and resource planning
    • Ongoing optimization recommendations
Deliverables & timeline

What you get

User Experience

  • Task-focused interface design
  • Clear failure state handling
  • User feedback and rating systems

User Interface

  • Explainable AI response presentation
  • Source citation and confidence display
  • Human escalation and override options

Technical Handoff

  • Comprehensive evaluation and testing suite
  • Safety guardrails documentation and monitoring
  • Performance dashboards and alerting systems
Typical timeline
Most AI pilots launch within 6-10 weeks; early proof-of-concept typically ready in 2-3 weeks.
Start a project
RAG search system architecture, copilot integration plan, and automation workflow diagram.
01

Outcomes, not output

We set measurable targets and ship against them—no black boxes.

02

Build to learn

Every sprint tightens activation, retention, or revenue.

03

Scale without bloat

Clean UI, clean code, fewer regressions.

Your success is our priority

  • Measurable business impact

    Every AI implementation ties directly to user satisfaction, cost reduction, or revenue metrics you can track.

  • Production-safe by design

    Built-in safeguards, evaluation systems, and human oversight reduce AI risks in business-critical applications.

  • Maintainable and scalable

    Clear data pipelines, monitoring systems, and evaluation frameworks make ongoing AI operations manageable.

Questions—answered

  • Which AI model and provider do you recommend?+
    We select models based on your specific use case requirements, balancing cost, latency, and quality trade-offs for optimal results.
  • Can you deploy AI on-premises or in private cloud?+
    Yes, we support both cloud and on-premises deployments with strict data handling protocols for sensitive information.
Contact band

Let’s talk practical ai implementation - rag & automation

Share what you’re building. We’ll return a scope, risks, and a price band within 48 hours.

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