Define your LLM, RAG & AI Agent strategy
AI should be an investment, not a cost center. Our audit pinpoints where value is real, measurable, and scalable for your organization — before you commit engineering resources.
Why start with an audit?
80% of AI projects fail due to poor scoping. Don't be part of that statistic.
Quantify real ROI
Identify exactly where time and cost savings are achievable before committing a single dollar to development.
Assess your data
Evaluate data quality, volume, and accessibility across your stack — ERP, CRM, internal databases — and surface gaps before they block delivery.
Prioritize impact
Don't build AI for AI's sake. Focus engineering effort on the use cases that generate measurable productivity gains for your team.
Our methodology
A structured discovery process built around your actual workflows, not theoretical use cases.
Discovery & Workflow Interviews
We spend structured time with your technical and operational teams to understand real workflows, friction points, and the low-value repetitive tasks consuming engineering and ops bandwidth.
- Stakeholder interviews (eng, ops, product)
- Current tooling and stack audit
Scoring & Feasibility Analysis
Every identified opportunity is scored on two axes: business impact (productivity gain, cost reduction) and technical complexity (data availability, model selection, infrastructure requirements). We evaluate your stack against LangChain/LlamaIndex/native APIs, model selection (OpenAI/Anthropic/open-source), and deployment trade-offs.
What's included in the audit
A complete scoping engagement to make informed build decisions, not AI for AI's sake.
Discovery workshops
Structured interviews with your operational and engineering teams to surface real friction points and identify high-volume repetitive tasks.
Data audit
Assessment of data quality, volume, and accessibility across your existing systems — ERP, CRM, internal documents, databases.
Impact vs. complexity matrix
Every use case scored on two axes: real business impact and technical complexity. You see clearly where to start and what to defer.
Quantified ROI estimate
Projected gains (time saved, cost reduction, quality improvements) over 12 and 24 months for each identified use case.
Technical stack recommendations
Model selection (OpenAI/Anthropic/open-source), orchestration layer (LangChain/LlamaIndex/native APIs), infrastructure, and integration trade-offs tailored to your context.
Actionable implementation roadmap
Phased plan with quick wins (weeks 1-4), mid-term projects, and a 12-month vision. Not a document that sits in a drawer.
What you receive
More than a PDF report — an operational roadmap your engineering team can act on immediately.
Workflow map
Overview of your operational flows with identified AI injection points.
ROI analysis
Quantified time and cost savings projection over 12 and 24 months.
Architecture & technical stack
Recommendations on models (LLMs), orchestration frameworks, and infrastructure — with rationale for each decision.
Deploy a RAG assistant on your internal knowledge base.
Client results
Real outcomes from our AI strategy engagements.
Distribution: 80% time reduction in supply chain tracking
We automated purchase tracking and supplier comparison for a distribution company using AI agents.
Read the case study → Case StudyResearch firm: -60% on report generation
Automated transformation of raw data into professional PowerPoint presentations using AI.
Read the case study → Case StudyEngineering firm: -75% on bid response time
Deployed AI agents for price extraction, specification analysis, and technical proposal drafting.
Read the case study → Case StudySaaS vendor: -50% support tickets with RAG
Deployed a RAG assistant to automate user support for a medical software platform.
Read the case study →Why an AI audit is non-negotiable
The risks of launching an AI project without proper scoping — and how to protect your ROI.
Read the article → Technical GuideAgentic RAG: when retrieval meets reasoning
A practical breakdown of agentic retrieval patterns and when to use them over standard RAG.
Read the guide → MethodologyLaunching an AI project: a realistic guide
Concrete steps from audit to production, including best practices and common pitfalls to avoid.
Read the guide → Related ServiceInternal RAG systems: build on your audit findings
Once the audit identifies knowledge-base automation as a priority, we build and deploy the RAG system for you.
See the service →Custom pricing — get a quote
Scoping workshop, workflow map, prioritization matrix, quantified ROI, and an actionable roadmap. Final pricing depends on scope (number of workflows, depth of data audit). Free 30-minute call to scope before any commitment.
- Free 30-min scoping call included
- Delivered in 2 to 4 weeks depending on scope
- Custom quote for SMBs and scale-ups
Frequently asked questions
Ready to define your AI strategy?
Book a free 30-minute scoping call to discuss your stack and see where an AI audit can unlock measurable ROI for your team.