Most of what gets labeled “AI consulting” is either vendor reselling or slide-deck strategy that never touches actual implementation. Our practice is different: we architect and build working AI systems, with a consistent emphasis on data privacy, local-first deployment, and measurable business outcomes. We don’t oversell capability and we don’t undersell the engineering effort required to do it properly.

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Local-First AI Strategy

Before you send a single byte to a cloud AI provider, you should know exactly what you’re sending and what the alternatives are. For many business workflows, a local large language model running on your own hardware can handle the bulk of the processing — classification, extraction, summarization, generation — at a fraction of the cost and without transferring sensitive data off-site.

We design AI architectures that make the local/cloud tradeoff deliberately. Local models (via Ollama and similar tooling) handle tasks that don’t require frontier-level reasoning. Cloud models with frontier capability handle the complex analysis — but only after sensitive data has been sanitized. The result is a system with strong privacy guarantees and access to best-in-class reasoning where it actually matters.

• AI workload assessment: what can run locally vs. what needs cloud inference
• On-premise LLM deployment with Ollama on existing GPU hardware
• Model selection and benchmarking for your specific task types
• Hybrid architecture design: local inference + sanitized cloud escalation
• Cost modeling: local compute vs. API spend at scale

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LLM Integration & Custom Pipelines

Connecting an LLM to your business data requires more than wiring up an API call. It requires understanding the data model, designing retrieval strategies that surface the right context, building prompt templates that produce consistent structured output, and validating that the outputs are trustworthy before they go anywhere downstream.

We build end-to-end pipelines: document ingestion (PDF, structured data, database exports), vector storage for semantic retrieval, prompt engineering and testing, output parsing and validation, and integration with your existing systems. We use Anthropic Claude, OpenAI, and local models depending on the requirements. Everything is built to be observable — you can see what the model received, what it returned, and why a decision was made.

• Document ingestion pipelines (PDF, structured data, database integration)
• Retrieval-augmented generation (RAG) with vector databases (ChromaDB, pgvector)
• Prompt engineering, testing, and version control
• Structured output parsing and downstream integration
• MCP (Model Context Protocol) server development for custom data access
• Evaluation frameworks: how do you know the model is performing correctly?

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Data Privacy & PII Protection

If your business handles personal information — customer records, financial data, health information, employee data — you have a legal and ethical obligation to know where that data goes when you start integrating AI. The answer cannot be “we assume the vendor handles it.”

We implement a rigorous PII sanitization layer using Presidio (Microsoft’s open-source framework, MIT license) combined with a local LLM for contextual detection. Presidio handles rule-based identification of standard PII patterns: names, SSNs, account numbers, addresses, phone numbers, email addresses. The local LLM catch layer identifies contextual PII that rule-based systems miss — an account number that was described in prose two sentences earlier, a transaction description that identifies a specific individual. Only the sanitized representation leaves your network. The mapping between sanitized identifiers and real data stays local and is never transmitted.

• PII audit: identify what personal data your AI pipelines currently expose
• Presidio deployment with custom recognizers for your data patterns
• Local LLM contextual PII detection layer (catch what rules miss)
• Anonymization and pseudonymization strategy design
• Data flow documentation for compliance and audit purposes
• Privacy-preserving pipeline architecture review

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AI Readiness Assessment

Before investing in AI tooling, it’s worth spending a few hours with someone who has built these systems to understand where AI will actually add value in your business versus where it will add complexity without benefit. The assessment is an honest conversation — we look at your workflows, your data quality, your compliance constraints, and your infrastructure, then give you a prioritized list of opportunities with realistic effort and ROI estimates.

Many businesses discover that their highest-value AI applications are also their least glamorous: automating a document extraction workflow that currently consumes 10 hours of staff time per week, or building a classification system that flags anomalies in a dataset that currently requires manual review. These aren’t the use cases that get covered in tech press, but they pay for themselves in weeks rather than years.

• Business process review: where does AI add real value?
• Data quality assessment: do you have the inputs AI needs to work reliably?
• Infrastructure readiness: hardware, GPU availability, network architecture
• Compliance mapping: what regulations govern your data and AI use?
• Prioritized opportunity matrix with effort and ROI estimates
• 90-day implementation roadmap for the highest-priority use cases

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