Our Services

Many organizations know AI could help their business, but they're not sure where to start—or which projects will actually deliver value versus which are just interesting experiments.

We begin every engagement with a structured assessment of your operations, data landscape, existing systems, and business goals. We identify specific workflows where AI can reduce costs, improve quality, or create new capabilities. The output is a prioritized roadmap that shows which projects to pursue first, what dependencies exist, and what investment is required.

This isn't generic consulting. We dig into the details: What data do you actually have? What quality is it? What regulations or privacy constraints apply? What does your team need to be able to maintain? The roadmap we deliver is tailored to your context and constraints.

Most businesses have workflows that involve copying data between systems, waiting for manual approvals, or performing repetitive classification and routing tasks. These workflows are slow, error-prone, and expensive—but they're also usually fixable.

We build intelligent automation that connects your existing tools—CRMs like Salesforce or HubSpot, communication platforms like Slack or email, databases, spreadsheets, Notion, Airtable, and more. Using tools like n8n and custom integrations, we create workflows that run automatically, with clear visibility into what's happening and built-in error handling.

Where it makes sense, we layer in AI for tasks like classification, summarization, data extraction, or decision routing. The result is faster execution, fewer errors, and freed-up time for your team to focus on work that actually requires judgment.

This is often the fastest way to see ROI from AI. You don't need custom models or complex infrastructure—just smart automation that connects what you already have.

Public AI models like GPT-4 or Claude are powerful, but they're generic. They don't know your industry terminology, your brand voice, your internal processes, or your specific domain expertise. For many use cases, that's fine. For others, it's a limiting factor.

We fine-tune language models and other AI systems using your proprietary data—internal documents, past communications, domain-specific knowledge, successful examples, and more. Using techniques like LoRA (Low-Rank Adaptation) and QLoRA, we can create models that behave like trained members of your team: they understand your context, follow your standards, and produce outputs that match your expectations.

This is particularly valuable when you need AI to handle tasks that require specialized knowledge—legal analysis, medical coding, financial advising, technical support, content creation with a specific voice, or any domain where accuracy and consistency matter more than generic capability.

Custom models can be deployed on your infrastructure (see Secure Hosting below) or accessed via private APIs. Either way, the model is yours—it's trained on your data, tuned to your needs, and no one else has access to it.

Your organization has valuable knowledge locked in documents, spreadsheets, wikis, past project files, email threads, and more. AI models don't inherently know any of that—but with retrieval-augmented generation (RAG), they can access it securely and use it to provide accurate, context-aware answers.

We build RAG systems that index your internal knowledge, create searchable embeddings, and integrate with AI models to retrieve relevant information on demand. When someone asks a question, the system finds the most relevant documents or passages, provides them as context to the AI, and generates a response grounded in your actual data—not hallucinated or generic.

This is powerful for internal knowledge bases, customer support systems, research and analysis workflows, onboarding new team members, and any scenario where you need AI that "knows" your proprietary information without requiring retraining.

Critically, RAG systems can be designed with clear privacy boundaries. Your documents stay in your storage, embeddings are created on your infrastructure, and retrieval happens without exposing raw data to third parties. We help you navigate the tradeoffs between performance, cost, and security.

Public AI APIs are convenient, but they come with tradeoffs: you don't control the infrastructure, you're subject to rate limits and pricing changes, your data passes through third-party servers, and you can't guarantee latency or availability. For some use cases, that's fine. For others, it's a dealbreaker.

We design and operate private hosting for AI models, embedding systems, and related infrastructure. This can run on your cloud account (AWS, Azure, GCP), on-premises in your data center, or on dedicated hardware we provision specifically for you. The goal is to give you control: over data flow, security boundaries, performance, and cost.

We focus on operational reliability—monitoring, observability, automated failover, and clear runbooks. We also design for cost efficiency, using techniques like model quantization, batching, and GPU sharing where appropriate. You get the benefits of private infrastructure without needing to build and maintain a specialized ML ops team.

This service pairs naturally with custom model development and RAG systems, but it's also valuable standalone—for example, hosting open-source models privately to reduce API costs or meet data residency requirements.

AI systems are not "set and forget." Models drift as underlying data distributions change. Embeddings become stale as your document library grows. Workflows break when integrated tools change their APIs. Performance degrades if you don't monitor latency, accuracy, and cost.

We offer ongoing maintenance and support retainers for organizations that want a long-term technical partner rather than a one-time vendor. This includes monitoring system performance, refreshing RAG embeddings as your knowledge base updates, retraining or fine-tuning models when accuracy slips, updating workflows when tools or business requirements change, and handling any infrastructure issues that arise.

We also proactively suggest improvements—new automation opportunities as they become clear, updated model versions, cost optimizations, and expansions to existing systems. The goal is continuous evolution: your AI systems improve alongside your business, rather than becoming technical debt.

This service is designed for organizations that view AI as core to their operations, not a one-off project. We prefer partnerships where we can help you grow capabilities over time rather than delivering something static and walking away.