Enterprise AI Feasibility Studies

An AI Feasibility Study is a time-boxed, highly technical engagement designed to mitigate the immense financial risk of building custom AI systems. Executives frequently demand "AI integration," but engineering teams struggle to quantify the feasibility. If your corporate data is stored as messy, un-OCR'd PDFs, a million-dollar RAG system will still hallucinate. Our service answers three critical questions: 1) Is your data clean enough for AI? 2) Can the current generation of LLMs solve your specific logical problem? 3) What will the cloud infrastructure and token inference cost at scale?

We provide the Mathematical Proof of Concept.

We deploy a structured 3-to-4 week engagement. We extract a subset of your data (e.g., 500 documents) and run it through advanced embedding models and OCR pipelines to quantify its density. We build a rapid, throwaway LangGraph/RAG pipeline and bombard it with 1,000 adversarial queries. We then utilize the Ragas framework (Retrieval Augmented Generation Assessment) to generate a mathematical score for Context Precision, Answer Relevancy, and Faithfulness.

Our feasibility studies remove emotion and rely entirely on data.

The AI Evaluation Lifecycle

We grade your proposed AI initiative across 4 critical pillars:

1. Data Readiness Assessment

• Format Consistency: Can your data be parsed deterministically? (e.g., Markdown vs. scanned images).
• Semantic Density: Does the data contain enough context for an embedding model to distinguish between Document A and Document B during a vector search?
• Data Governance: Mapping where PII (Personally Identifiable Information) lives and determining if it requires an expensive redaction pipeline before hitting the LLM.

2. Model & Architecture Selection

• Open-Source vs. Closed-Source: Determining if you actually need GPT-4o, or if a fine-tuned, self-hosted open-source model (like Llama 3 8B) can achieve the same accuracy for 1/10th the cost.
• Pipeline Complexity: Deciding if a simple RAG implementation is sufficient, or if the problem requires a complex, multi-agent LLM Orchestration state machine.

3. Mathematical Evaluation (Ragas)

We run the PoC output against automated evaluator LLMs to generate strict metrics:

• Faithfulness: Does the answer directly derive from the source data, or did the LLM hallucinate external facts?
• Answer Relevancy: Did the AI actually answer the user's question, or did it dodge it with a generic summary?
• Context Precision: Did the vector database return the correct document in the #1 position, or the #8 position?

4. Unit Economics & ROI

• Token Modeling: We calculate exact Input/Output token estimates. "If 5,000 users ask 3 questions a day, your Azure OpenAI bill will be exactly $4,200/month."
• Latency Projections: Predicting Time-to-First-Token (TTFT) to determine if the AI is fast enough for real-time voice or if it must be relegated to asynchronous web chat.

At the conclusion of the 3-to-4 week sprint, you receive:

1. The Feasibility Report: A definitive "GO / NO-GO" recommendation. We will explicitly tell you if your data is too poor to support the project.
2. The Economic Model: A detailed spreadsheet outlining projected cloud hosting and API token costs at 1k, 10k, and 100k Monthly Active Users.
3. The Prototype: Access to a secure web environment where your executives can physically interact with the rapid Proof of Concept.
4. The Engineering Blueprint: If the project is a "GO", we provide the exact architectural specifications, timeline, and budget required to move the PoC into enterprise production.

• Isolated Sandboxes: We conduct feasibility studies in highly secure, isolated AWS VPCs.
• Zero Training Policies: We strictly utilize enterprise API endpoints (Azure OpenAI, AWS Bedrock) that guarantee your proprietary test data is never used to train the provider's foundational models.
• Data Destruction: If the project is a "NO-GO", we execute a cryptographic wipe of all test data, vector databases, and cached prompt histories.

• RAG Development: If the study is successful, we execute this service to build the production system.
• Architecture Review: If your current software cannot support an AI integration, we audit the core system first.
• Prompt Engineering: Refining the system instructions developed during the PoC for maximum production security.