Your team repeats the same work every week
Lead routing, inbox follow-ups, spreadsheet updates, invoice checks, report generation, and status pings keep stealing operator hours.
Deltabits maps repetitive business work, designs private AI automation architecture, and ships agents that can act across your tools with approval rules and readable logs.
Deployable on AWS, private cloud, or client-controlled provider accounts
Designed for strict data boundaries and human approval
OpenClaw-style agents plus a self-hosted automation layer
Audit output preview
12 workflows mapped, 3 pilots ranked, connector paths listed, approval gates defined, and estimated operator hours saved.
Clients and partners
Commerce, media, AI, consulting, and public-sector operators have all needed the same thing: software that turns repeated backend work into a faster operating system.
Reason Clothing
Otto Media
GIA AI
Viral Idea Marketing
RSG Consulting
Government of Barbados
Government of The Bahamas
Reason Clothing
Otto Media
GIA AI
Viral Idea Marketing
RSG Consulting
Government of Barbados
Government of The BahamasThe operating gap
Most companies have prompts, subscriptions, and experiments. What they do not have is a private operator that can safely move work through the tools the business already depends on.
Lead routing, inbox follow-ups, spreadsheet updates, invoice checks, report generation, and status pings keep stealing operator hours.
A prompt can help with a draft. It cannot reliably update your CRM, reconcile a payment, send a clean handoff, and leave an audit trail.
CRM, email, files, billing, support, and internal docs all hold part of the truth. Automation needs the whole operating context.
Most AI rollouts start with convenience. We start by deciding what stays private, what can use provider APIs, and what needs human approval.
What Deltabits builds
The stack is intentionally pragmatic: agent control, automation execution, connector paths, and operator oversight.
Private by design
Run local or self-hosted models for sensitive workflows, use client-owned provider keys where appropriate, or combine both with explicit data boundaries.
Action layer
OpenClaw-style agents handle intent and context. The automation layer executes approved actions across CRM, inbox, files, billing, support, and databases.
Connector layer
Use self-hosted automation connectors for common business apps, webhooks, APIs, and custom adapters when a client stack needs a specific path.
Operator control
Critical actions can require human approval, every workflow gets a readable action trail, and pilots ship with clear boundaries before broader rollout.
Audit process
We map the work before building the system. The audit identifies the workflows that are worth automating, the data that must stay private, and the first pilot that can ship without unnecessary risk.
Review the auditWe inspect the workflows your team repeats: inboxes, CRMs, spreadsheets, finance ops, support queues, docs, and handoffs.
We classify which data should stay on private infrastructure, which actions need approval, and which tools can use client-owned API keys.
We score opportunities by hours saved, integration complexity, business risk, and speed to a reliable first win.
You receive a pilot plan with stack choices, connector paths, operating rules, and a phased implementation estimate.
Channels and connectors
Agent channels handle requests from chat. The automation layer handles app actions, webhooks, APIs, and custom adapters.
Field notes

A practical operator guide for picking the first AI workflow: repetitive enough to matter, safe enough to ship, and connected enough to prove value.
Read insightBuilder story
Deltabits comes from years of building products, applying to YC, getting rejected, studying early AI models, and learning where businesses actually lose time: repeated backend work.
Read the storyThe work began with a practical obsession: build real software, learn from the system, and keep shipping until the product teaches the next step.
During the COVID-19 shutdowns, the founder built BakersLoaf for a cloud kitchen, applied to YC for the first time, got rejected, and kept building.
Early OpenAI models became a signal. That curiosity became picsy.art, an early AI art generation app built before generative images became mainstream.
The next product focused on book summaries for busy professionals. Another YC rejection followed, along with a clearer understanding of AI-assisted work.
Klippie automated trailer and clip extraction for video marketing teams, turning long-form content into usable assets faster.
After years of products, rejections, pivots, and AI experiments, the mission became clear: help businesses automate private operational work without handing away sensitive context.
Start with the audit
Get a workflow map, private AI architecture, connector plan, and pilot roadmap before committing to a build.