Enterprise AI Implementation & Advisory

Professional engineering services to transition AI prototypes to production-grade systems, focusing on RAG, agentic frameworks, and secure private-cloud deployment.

We provide the engineering expertise to bridge that gap, deploying defensible architectures into your own ecosystem.

Get 5-Point AI Security Checklist:

Engagement Models

We offer three tiers of implementation and advisory:

1

The Implementation Sprint

Moving from architectural blueprints to a live, secure environment with speed.

The Goal: Deploying a Private-Cloud Alpha

Scope of Work:

  • Core Build: Deployment of our pre-validated RAG or Agentic frameworks (LangGraph/Cyclic DAGs) customized for your proprietary data.
  • Private Cloud Integration: Deployment into your AWS, Azure, or GCP environment to ensure data residency and security.
  • Security First: Infrastructure as Code setup to ensure all data processing remains within your secure VPC.
  • Proprietary Data Ingestion: Connection to your specific internal data stores and API ecosystems.

Targeting a functional Alpha deployment in 14 business days, contingent on infrastructure readiness.

2

Data Pipeline Engineering

Transform raw, unstructured data into high-fidelity assets for your LLMs.

The Goal: High-Quality Structured Data

Scope of Work:

  • Data Structuring & Cleaning: Transforming messy PDFs, documentation, and legacy logs into optimized Markdown or JSON formats for model consumption.
  • Automated PII & Anomaly Detection: Implementing automated layers to detect and redact sensitive information before it reaches the model API.
  • Semantic Chunking & Indexing: Designing sophisticated data ingestion pipelines that optimize how information is retrieved during the RAG process.

Targeting initial pipeline architecture and data cleaning protocols established within 5 business days. Implementation depends on dataset volume.

3

Reliability & Security Audit

Audit cost, latency, and reliability of your AI pipelines.

The Goal: Optimizing Existing AI Pipelines

Scope of Work:

  • Performance Tuning: Systematic reduction of inference latency and token expenditure through quantization (GGUF/AWQ) and prompt-chain optimization.
  • Evaluation Framework: Implementing RAGAS and custom LLM-as-a-judge metrics to quantify system faithfulness.
  • Reliability Hardening: Building deterministic guardrails to eliminate hallucinations in mission-critical workflows.

Targeting comprehensive audit and optimization reports delivered within 7 business days. Details depend on the system infrastructure.

Contact UsSchedule a Briefing 

The Execution Process

Step 1. Technical Briefing

60-minute technical discovery call. You can expect:

  • Evaluate your current data infrastructure and cloud readiness.
  • Specify technical stack (model selection, RAG vs. Fine-tuning etc) for your business objectives.
  • General Q&A addressing security, latency expectations, integration constraints, and more.

Step 2. Project Scoping

After our briefing, I provide a formal Statement of Work (SOW). You can expect:

  • Deliverables like a list of features, agents, and integrations.
  • Timeline and milestones from the kickoff to deployment.
  • Fixed quote.

Step 3. Execution

After SOW, project kick-off with direct engineering access. You can expect:

  • A mid-point review to refine reasoning and tool-calling logic before finalization.
  • Full handover of production-ready source code and private cloud deployment.
  • Documentation for your team to ensure long-term maintenance and monitoring.
Contact UsSchedule a Briefing