Intelligent Automation (AI)

Intelligent Automation (AI) Services

Ship AI assistants & automated workflows safely

We implement AI copilots and automated workflows powered by Claude Code, Codex, and OpenAI APIs. Using robust evaluation and guardrails, we deliver reliable assistants that integrate with your data and tools—safely and measurably.

What We Build

AI Assistants & Copilots

  • Customer support chatbots
  • Internal knowledge assistants
  • Code generation & review tools
  • Document analysis & summarization
  • Data extraction & classification
  • Multi-modal AI applications

Retrieval-Augmented Generation (RAG)

  • Knowledge base integration
  • Semantic search systems
  • Vector database implementation
  • Context-aware responses
  • Document Q&A systems
  • Enterprise search enhancement

Workflow Automation

  • Intelligent process automation (IPA)
  • Email & document processing
  • Data enrichment pipelines
  • Report generation & distribution
  • Multi-step agent workflows
  • Tool-calling & API integrations

Governance & Safety

  • Model evaluation frameworks
  • Prompt security & injection prevention
  • Content filtering & moderation
  • Bias detection & mitigation
  • Usage monitoring & alerting
  • Red-teaming & adversarial testing

AI Platforms We Work With

Anthropic Claude

  • Claude 3.5 Sonnet (Opus, Haiku)
  • Claude Code for development tasks
  • Long context windows (200K tokens)
  • Constitutional AI safety

OpenAI

  • GPT-4, GPT-4o, GPT-4o mini
  • Codex for code generation
  • Embeddings & fine-tuning
  • DALL-E for image generation

Open Source & Others

  • Llama, Mistral, Mixtral
  • Self-hosted model deployment
  • Google Gemini
  • Custom model training

Technology Stack

LLM Frameworks: LangChain, LlamaIndex, Haystack
Vector Databases: Pinecone, Weaviate, Qdrant, pgvector
Orchestration: LangGraph, Semantic Kernel, CrewAI
Evaluation: LangSmith, Weights & Biases, custom eval frameworks
Deployment: FastAPI, Modal, Replicate, AWS Bedrock
Monitoring: LangFuse, Helicone, custom observability

Our AI Implementation Process

  1. AI Readiness Assessment – Evaluate your use case, data quality, success metrics, and compliance requirements.
  2. Proof of Concept – Build a working prototype to validate the approach and measure baseline performance.
  3. Data Pipeline – Set up data ingestion, preprocessing, embedding, and retrieval infrastructure.
  4. Model Development – Select models, engineer prompts, implement retrieval, fine-tune if needed.
  5. Evaluation & Testing – Create test sets, run evals, red-team for security, measure accuracy/latency/cost.
  6. Production Deployment – Deploy with monitoring, logging, rate limiting, and fallback mechanisms.
  7. Continuous Improvement – Monitor performance, collect feedback, iterate on prompts and architecture.

Security & Compliance

We implement comprehensive security and compliance measures for all AI systems:

  • Data Privacy: PII detection, anonymization, GDPR compliance, data retention policies
  • Prompt Security: Injection prevention, content filtering, malicious input detection
  • Access Control: Authentication, authorization, rate limiting, audit logs
  • Model Governance: Version control, model cards, evaluation metrics tracking
  • Transparency: Explainability, confidence scores, human-in-the-loop workflows
  • Data Processing Addendum (DPA): Available for all enterprise clients

Case Example

Customer Support AI Assistant

Challenge: SaaS company handling 1,000+ support tickets/month, 70% repetitive questions.

Solution: RAG-powered chatbot using Claude 3.5 Sonnet with company docs, past tickets, and product data.

Results:
• 60% ticket deflection rate
• 5-minute average resolution time (vs. 2 hours)
• 95% customer satisfaction score
• 50% reduction in support costs

AI Readiness Assessment

Schedule a free consultation to explore how AI can transform your business operations.

Get Started
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