The technology stack, the full range of use cases from simple to advanced, and an honest look at who gets the most value from forward-deployed AI engineering.
The AI landscape changes monthly. New models ship, pricing shifts, capabilities expand. Our job is to architect systems that use the right tool for each sub-task and can adapt as the landscape evolves — without rewriting your entire stack.
We deploy the right model for each task. GPT-4o for complex reasoning and analysis. Claude for long-document processing and nuanced language understanding. Gemini for multimodal inputs and large context windows. Llama and Mistral for high-throughput, cost-sensitive tasks that can run on your own infrastructure. Each sub-task in a workflow gets the model that's best suited for it, not a one-size-fits-all choice.
We build multi-agent workflows using LangChain and LangGraph for task routing, state management, and agent coordination. Vector search with Pinecone or pgvector for retrieval-augmented generation. Document processing with Textract, Document AI, or custom vision pipelines depending on your document complexity. Everything deploys on your cloud — AWS Bedrock, Azure OpenAI, or GCP Vertex AI — behind your firewall.
Enterprises run on documents. Invoices, contracts, applications, regulatory filings, shipping manifests. We build extraction pipelines using AWS Textract, Google Document AI, and custom OCR models to pull structured data from unstructured inputs — regardless of format, layout, or scan quality. Extracted data flows directly into your existing databases and ERPs through secure API integrations.
AI in production needs the same rigor as any other critical system. We instrument every workflow with LangSmith for trace-level LLM observability, Datadog or Grafana for infrastructure monitoring, and custom dashboards that show your ops team exactly what the system is doing, how accurately it's performing, and where human review is needed.
Every enterprise has work like this. Some of it is simple and can be automated in weeks. Some of it is complex and requires deep integration. We handle the full spectrum.
Thousands of inbound emails daily — customer requests, vendor inquiries, internal escalations — all landing in shared inboxes where someone manually reads, categorizes, and forwards each one. An LLM-powered classifier reads the email, identifies intent and urgency, extracts key entities, and routes it to the right team or triggers an automated response. Deploys in weeks.
Your AP team manually re-keys line items from PDF invoices into your ERP. Different vendors, different formats, different layouts. A document processing pipeline reads every invoice — scanned, digital, handwritten — extracts line items, PO numbers, tax amounts, and payment terms, then writes the structured data directly into SAP, Oracle, or NetSuite. No human re-keying required.
Legal teams reviewing hundreds of vendor contracts for non-standard clauses, liability exposure, and compliance gaps. Each review takes 2–4 hours of attorney time. A multi-agent pipeline ingests the contract, compares it against your approved clause library using vector similarity search, flags deviations with risk scores and plain-language explanations, and generates a summary brief for the attorney. Review time drops to 15 minutes for standard contracts.
New customer onboarding that requires collecting documents, verifying identities, running credit checks, provisioning accounts across multiple systems, and sending status updates. Currently a 5–7 day process involving three departments. An orchestrated agent workflow handles document collection, runs verification against third-party APIs, provisions accounts in your CRM and billing systems, and keeps the customer informed — reducing onboarding to same-day.
Thousands of loan applications daily, each requiring an analyst to manually review bank statements, tax returns, pay stubs, and identity documents. Data is pulled from unstructured PDFs, cross-referenced across multiple sources, and entered into a risk scoring model. An intelligent pipeline ingests all documents, extracts and normalizes financial data, runs automated trend analysis, scores against your existing risk model, and queues decisions. High-confidence applications auto-approve. Edge cases route to analysts with pre-assembled context and a recommended decision.
Compliance teams manually tracking regulatory changes across multiple jurisdictions, cross-referencing updates against internal policies, and updating procedures across the organization. An always-on monitoring system ingests regulatory feeds, identifies changes relevant to your industry and geography, maps them against your existing policy database using retrieval-augmented generation, generates impact assessments, and routes required policy updates to the appropriate compliance officers with draft revisions.
Insurance claims that touch intake, document collection, damage assessment, coverage verification, fraud detection, adjuster assignment, and payment authorization. Currently a 14–21 day lifecycle with handoffs between six departments. A multi-agent system handles the entire pipeline: ingesting the claim, extracting information from photos and documents using vision models, verifying coverage against the policy database, running fraud pattern detection, calculating settlement amounts, and routing approvals through your existing authorization hierarchy. Standard claims resolve in hours, not weeks.
Global supply chains with thousands of daily shipments requiring customs documentation, regulatory compliance across jurisdictions, carrier coordination, exception handling, and real-time customer visibility. A multi-agent architecture monitors shipments in real time, auto-generates customs declarations using document-parsing agents, cross-references regulatory requirements per country, detects and resolves exceptions before they cause delays, coordinates with carrier APIs, and provides stakeholders with natural-language status updates. The system that used to require a room full of coordinators now runs autonomously with human oversight on exceptions only.
You're the director or VP who watches your team spend 60% of their time on work that should be automated. You've tried to push for change but the last three tools you piloted didn't survive integration testing. You need a partner you can point to and say: "This person will come in, build it, and make it work. I've seen their results."
We give you the architecture blueprint, the ROI model, and the case study evidence to build an airtight internal proposal. When you present this to leadership, you're not pitching a tool — you're presenting an engineering engagement with verified outcomes.
You're the CIO, CTO, or head of digital transformation responsible for vetting AI investments. You've seen vendors who demo well and fail in production. You need to understand exactly how the system is architected, how it connects to your existing stack, what happens when a model provider changes their API, and how you maintain control after deployment.
We'll do a technical deep-dive with your engineering and security teams before you commit to anything. No salespeople in the room — just our engineer explaining the architecture, the data flows, and the failure modes. Ask the hard questions. That's what the call is for.
You're the COO, CFO, or business unit leader who signs off on the engagement. You're not interested in the technology — you're interested in the operating leverage. How many FTEs does this free up? What's the payback period? What happens if it doesn't work?
Every engagement starts with a two-week diagnostic that produces a detailed ROI model against your actual numbers. You'll know the projected savings, the implementation timeline, and the risk profile before you commit to a build. And the code belongs to you — if the engagement ends, the system stays.
Most operations leaders already know the number. Put it in and we'll show you what changes when the manual work goes away.
Tell us about the workflow. We'll tell you what we'd build and whether the math works.
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