We automate document workflows, classification, extraction, summarization and multi-step operations using LLMs, vector search and workflow engines. Humans stay in the loop where it matters.
AI automation uses large language models, machine learning and workflow engines to automate business operations — document processing, classification, data extraction, summarization, email triage and multi-step decision workflows — with humans in the loop where quality matters.
Extract, classify, summarize invoices, contracts, forms, emails.
AI + Temporal, n8n, Make or custom orchestration.
Classify, route, draft replies, escalate.
Clean, dedupe and enrich CRM or ops data with LLMs.
Tool-using agents for real, bounded tasks — not vaporware.
A plain answer up front. We'd rather not sell you something you don't need.
Pricing is quoted after discovery based on scope, team shape and delivery timeline.
The people you meet in discovery stay involved through architecture, delivery and launch.
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
Repos, infra, analytics and documentation live in your accounts from the beginning.
Real delivery examples tied to this service area, so buyers can move from claims to shipped work.
An operations team automated intake, classification and escalation across email, documents and support queues without trying to remove humans from quality-sensitive decisions.
A product team added multiple LLM-powered workflows into an existing SaaS platform with model routing, prompt controls and request-level observability.
A product team replaced a brittle Python knowledge surface with a grounded Next.js and RAG stack to improve onboarding and support resolution.
“The automation worked because Cuibit did not try to remove judgment from the wrong places. The workflow got faster, but the team still kept control where quality really mattered.”
“What we needed was not a demo bot. We needed AI features inside the product with cost visibility and sensible controls, and Cuibit built the layer we could actually operate.”
Supporting articles that help buyers understand the tradeoffs, architecture choices and implementation details behind this service area.
AI-powered product development is changing how SaaS, ecommerce, and software teams move from idea to release in 2026. This practical playbook explains where AI helps, where governance matters, and how to measure real business value.
A practical May 2026 guide to AI chatbot development cost covering pricing ranges, RAG, LLM integration, workflow automation, hidden costs, and what businesses should actually budget for.
WooCommerce is becoming more AI-ready through MCP, canonical product and order abilities, and Claude workflows. This 2026 guide explains how stores should prepare product data, performance, checkout, permissions, and automation safely.
Document extraction, email triage or meeting-notes-to-CRM are usually the fastest wins — measurable, low-risk, big time savings.
Bounded agents on well-scoped tasks — yes, very well. Open-ended 'do my job' agents — no, not yet in 2026.
Time saved per run, error rate vs human baseline, $ cost per successful run, quality audit score.
Pricing is quoted after discovery based on scope, team shape and delivery timeline. A focused single-workflow automation, a multi-step agent with integrations and an enterprise document-AI rollout are each scoped differently, so we share a written proposal after discovery.
Yes — we integrate with CRMs (Salesforce, HubSpot), helpdesks (Zendesk, Intercom), project management (Jira, Linear), document stores (Google Drive, SharePoint) and custom internal systems via API.
Most automation projects show measurable time savings within the first 2–4 weeks of deployment. We define success metrics before build starts so ROI is tracked from day one.
Tell us about your project. A senior strategist replies within one business day — with a written first take.