Services

AI & LLMs

Leverage the power of AI and LLMs to enhance your applications and drive initiation. We design secure, private, and domain-specific AI systems — including on-premises or hybrid deployments — fine-tuned on your own data within your own infrastructure.

  1. 1
    Opportunity Discovery
    Goal: Understand the business case for AI and define high-value use cases.
    • Facilitate workshops to map out pain points and inefficiencies
    • Identify tasks suitable for AI augmentation (classification, generation, retrieval)
    • Evaluate feasibility based on data availability and complexity
    Deliverables:
    • AI Opportunity Map
    • Feasibility & ROI Brief
    • Use Case Prioritization Matrix
  2. 2
    Data Strategy & Preparation
    Goal: Ensure clean, structured, and accessible data for fine-tuning or retrieval-based AI.
    • Audit existing data sources (structured, unstructured, semi-structured)
    • Define data labeling and augmentation strategies
    • Establish secure data pipelines with validation
    Deliverables:
    • Data Inventory & Access Plan
    • Data Cleaning Scripts
    • Labeled Dataset Samples
  3. 3
    Model Selection & System Design
    Goal: Select appropriate LLMs and design the system architecture.
    • Evaluate open-source, proprietary, and hosted LLMs (e.g. Mistral, Ollama, OpenAI, Claude)
    • Design for on-premises or private deployment if needed
    • Define integration points and fallback logic
    Deliverables:
    • Model Evaluation Summary
    • System Architecture (LLM-centric)
    • Inference Strategy & Latency Plan
  4. 4
    Iterative Model Integration
    Goal: Integrate the AI model into workflows and refine based on user feedback.
    • Develop APIs or middle layers for model invocation
    • Embed into business flows and user interfaces
    • Collect user feedback and refine prompts or RAG components
    Deliverables:
    • Working API or Component Integration
    • Prompt Library or RAG Templates
    • UX Feedback Summary
  5. 5
    Evaluation & Safety Testing
    Goal: Test for quality, bias, hallucination, and guardrail compliance.
    • Create automated and manual evaluation suites
    • Perform red-teaming and edge case exploration
    • Define fallback/override mechanisms and escalation paths
    Deliverables:
    • Evaluation Reports
    • Safety Checklist & Fixes
    • Bias Mitigation Notes
  6. 6
    Deployment & Monitoring
    Goal: Deploy with observability, privacy, and compliance controls in place.
    • Deploy via containerized or air-gapped infrastructure (on-prem/cloud hybrid)
    • Enable prompt-level logging and performance tracking
    • Implement user behavior monitoring for feedback loops
    Deliverables:
    • Deployment Plan
    • Monitoring Dashboard (tokens, latency, usage)
    • Data Privacy Compliance Checklist
  7. 7
    Continuous Learning & Iteration
    Goal: Improve model relevance, responsiveness, and impact over time.
    • Collect post-launch usage data and feedback
    • Fine-tune models on updated or domain-specific data
    • Plan for feature evolution and retraining schedule
    Deliverables:
    • Fine-Tuning Roadmap
    • Feature Backlog & User Suggestions
    • Retraining Schedule

Need a tailored solution? Let’s build your agile, scalable software together.

Whether you need solutions architecture, app development, integrations, automation, hosting, or AI-powered systems, we assemble custom teams to accelerate your vision with Agile delivery.

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