Search

This page powers the site SearchAction in JSON-LD. It returns curated routes, not arbitrary site search.

Showing all curated routes.

• Home

  AI-first build studio for startups and small teams. We ship Rapid POCs in 14 days so you can prove an idea on your data before you fund a bigger build.

  /

• Services hub

  Overview of five consulting pillars.

  /services

• Rapid POC

  Sandboxed prototype offer and handoff path.

  /rapid-poc

• Case studies

  Illustrative engagement patterns.

  /case-studies

• Blog

  Implementation notes for buyers and operators.

  /blog

• About

  Mission, approach, and team placeholders.

  /about

• Contact

  Lead form and optional calendar booking.

  /contact

• AI Workflow Automation

  End-to-end automations across the tools you already use, with model judgment where rules alone are not enough.

  /services/workflow-automation

• Custom AI Agents

  Agents that take real actions in your tools, with memory, fallbacks, and a human in the loop where it matters.

  /services/ai-agents

• Voice AI Agents

  AI voice agents for inbound calls, qualification, scheduling, and callbacks. Real-time, interruptible, and tied to your CRM.

  /services/voice-ai-agents

• Agentic Knowledge Assistants

  Internal and customer-facing assistants that actually look things up, double-check themselves, and cite sources.

  /services/rag-chatbots

• Intelligent Document Processing

  Your invoices, receipts, contracts, and claim forms read and routed automatically, with confidence scoring and human review on the close calls.

  /services/intelligent-document-processing

• Unstructured Data Pipelines

  Your messy data, made useful. PDFs, email threads, voice notes, photos, and spreadsheets turned into structured records your tools can use.

  /services/unstructured-data

• From adjuster email to structured claim intake at scale

  This pattern is for carriers where adjusters and third parties send facts as email threads and attachments, not as clean ACORD feeds. The goal is reliable structured records for routing, reserving, and downstream fraud checks, without asking adjusters to retype what they already wrote.

  /case-studies/specialty-insurer-unstructured-claim-email

• Freight booking automation across six carrier portals

  This pattern fits teams where capacity checks and booking confirmations require logging into multiple carrier systems that were never meant to integrate cleanly. The goal is fewer clicks for operators, fewer missed slots, and a replayable record when a carrier UI changes.

  /case-studies/logistics-freight-booking-across-carrier-portals

• Two hundred thousand pages a month of remittance advice and EOBs

  This pattern is for revenue cycle teams where payer PDFs and faxes arrive in bulk and posting accuracy is non-negotiable. The goal is high straight-through processing with a tight human review surface on the fields that actually drive cash.

  /case-studies/healthcare-billing-remittance-eob-idp

• A tier-1 agent that resolves account and billing issues end-to-end

  This pattern fits SaaS teams where tier-1 tickets repeat: invoices, seat counts, plan mismatches, and refund policy questions. The agent reads account state with least privilege, proposes actions within policy, and escalates when confidence drops or a human must approve money movement.

  /case-studies/b2b-saas-tier-one-support-agent

• Fifteen years of manuals and tickets, searchable with citations

  This pattern is for teams where technicians ask the same questions across plants but answers depend on machine revision, region, and superseded bulletins. The assistant must cite sources, respect access control, and refuse when evidence is weak, because wrong torque is not a branding problem.

  /case-studies/manufacturing-equipment-manuals-rag-assistant

• Ten days of evidence replaced a six-month slide contest

  This pattern is for leadership teams stuck between long RFP cycles and the need to see real outputs on sensitive internal samples. A Rapid POC is a sandboxed build (landing page plus app plus backend) that proves integration risk, latency, and accuracy early enough to matter.

  /case-studies/fintech-rapid-poc-vendor-selection

• What is a Rapid POC, and when should you run one instead of an RFP?

  A Rapid POC is a sandboxed working build on your real systems and a bounded slice of your real data, designed to answer procurement questions that documents cannot. An RFP still has a role when compliance requires apples-to-apples comparisons, but it is a poor primary tool for AI because the risk is behavioural (models under your traffic, on your documents) and not a feature matrix.

  /blog/rapid-poc-vs-rfp

• Unstructured data: the five places it hides in your business

  Unstructured data is any payload where meaning is not already in neat rows. Email bodies, PDF contracts, call recordings, images from the field, and the long tail of notes fields your teams misuse because your structured schema never matched reality. If you only warehouse structured tables, you are flying half blind on what actually happened in operations.

  /blog/unstructured-data-five-hiding-places

• When to use RAG versus fine-tuning versus an agent in May 2026

  RAG answers questions from a corpus you control and can cite. Fine-tuning shapes model behaviour and small specialised tasks when you own training signal. Agents plan steps and call tools under policies. Most production systems compose two of these. The failure mode is picking the buzzword instead of naming the decision the software must make.

  /blog/rag-vs-fine-tuning-vs-agents-2026

• IDP in 2026: what changed, and what did not

  Intelligent document processing (IDP) is the discipline of turning documents into decisions. Classify, extract, validate, route, and post, with measurable straight-through processing. In 2026, layout-aware vision-language models raised accuracy ceilings on ugly PDFs, but the hard parts remain validation, drift, and the economics of human review.

  /blog/idp-in-2026

• How to scope an AI workflow automation project so it actually ships

  AI workflow automation is only partially a model problem. It is mostly an integration and exception problem: your systems expose incomplete APIs, humans make judgment calls the model cannot own, and peak traffic looks nothing like a pilot folder. A scope that ships names states, owners, metrics, and rollback before it names a model.

  /blog/scope-ai-workflow-automation-that-ships

• AI agent failure modes, and how we design around them

  Production agents fail in predictable ways. They call the wrong tool with plausible arguments, loop until budgets trip, escalate too late or too early, and hide API errors behind confident language. Production agents need policy matrices, typed tools, traceability, and kill switches. Not a prettier chat UI.

  /blog/ai-agent-failure-modes-we-design-around