AI systems for ambitious companies
Connect your company data. Ship agentic workflows in days, not weeks.
Loming plugs into the CRM, drive, mailbox, and warehouse you already run, builds a unified knowledge layer on top, and turns it into production agents your team can use in minutes — with the governance, audit trails, and review loops enterprises actually need.
< 14 daysFrom kickoff to a working pilot
6 monthsTypical path to positive ROI
40+Typed tools shipped in production
2AI products of our own running live
The knowledge layer
One unified layer over the data you already have.
Most AI projects stall because nobody owns the data plumbing. Loming's knowledge layer connects your existing systems with read-scoped credentials, respects your permission rules, and keeps everything observable. The agents we build on top inherit that grounding by default — which is why they ship fast and stay correct.
Native connectors for the systems above, plus a typed-tool API for anything custom — your internal CRM, in-house data marts, or partner endpoints all plug in the same way.
What we build on top
Reports, search, and workflows your team can use the day they land.
We don't hand you a model and a chat box. Each output is a real piece of software — shaped to your data, your processes, and the way your team already works. You get a system, not a prototype.
Report generation
Board-ready reports in minutes.
Source the data, run the analysis, branded output. Word, PowerPoint, Excel — written from your live systems, not last quarter's snapshot.
- Cited sources
- Brand-consistent
- Re-runnable
Natural-language search
Ask your company anything.
One prompt across CRM, drive, mailbox, and warehouse. Every answer comes back grounded in the document, message, or row it came from.
- Citations on every line
- Permission-aware
- Audit logged
Custom workflows
Agents that finish the work.
Production-grade pipelines composed from your real processes — not demoware. Read, decide, write, hand off, and escalate inside the tools your team already trusts.
- Typed tools
- Review gates
- Drift monitoring
The operating layer
A governed loop from intent to action.
Every turn passes through policy, context, memory, tool execution, logging, and human review. The result is software your team can trust, monitor, and improve — not a black box that worked once in the demo.
Web chat
Email
Slack
Agent harness
Memory
Tools
Teammates
Project state
Reply sent
How engagements work
Eight weeks from messy workflow to a system in production.
01Week 1–2
Strategy
We shadow the real workflow, map the highest-leverage automation wins, and write the build plan. A fractional CAIO sits inside your team for the engagement.
02Week 3–4
Knowledge layer
We connect your CRM, drive, mailbox, warehouse, and chat into one unified layer. No migration — it sits on top of your existing stack, permission-aware from day one.
03Week 5–8
Build
Production agents, copilots, report generators, and automations land inside the systems your team already uses. Every turn is observable from the first day.
04Ongoing
Operate
Model upgrades, drift monitoring, and refinements are included. Your systems improve while you sleep — we keep them sharp as the underlying models evolve.
Proof in production
We ship our own AI products, then bring the same patterns to client systems.
Agent operations platform
HoEasy
A multi-tenant agent platform with memory, governance, skills, and execution nodes that route across web, Slack, WhatsApp, and email — so one workflow can show up everywhere your team works.
Per-account governanceHybrid memorySkill catalogMulti-channel
AI creative production studio
Muvaira
A directed AI studio for long-form stories, comics, and video chains where continuity and review matter. Scene state, frame continuity, and a credit ledger keep the creative loop tight.
Scene stateFrame continuityCredit ledgerReview loops
In their words
The systems we ship hold up under real workload.
“Loming shipped working software, not slide decks. Inside three weeks we were running a workflow that used to eat half a person's week.”
“The knowledge layer is the bit no one else gets right. Our reps ask plain questions and get answers grounded in the actual CRM, with sources.”
“Audit trails, permissioning, and the review gates were there on day one. That is what made it deployable for us, not a weekend toy.”
Security
Secure by design.
Audit trails, data isolation, and permission inheritance ship with the platform — not retrofitted after the pilot. Every action a model takes is scoped to the user behind it, logged in full, and reviewable before it touches production.
FAQ
The questions enterprise buyers actually ask.
What does "connect to our company data" actually mean?
We layer on top of your existing CRM, drive, mailbox, warehouse, and chat — using read-scoped credentials, your permission rules, and your retention policy. No migration, no lock-in. Your data stays where it lives.
How do you ship in days, not weeks?
We reuse a typed-tool harness across engagements: agent runtime, memory, governance, multi-channel delivery, observability. The new work each time is the connectors, the workflow logic, and the review UI — not the plumbing.
How do you keep the system from drifting as models change?
Every agent turn is logged with inputs, tools, outputs, and human decisions. We replay traces against new model versions, gate the upgrade behind regressions, and you get the better model without breaking the workflow.
Will my team have to learn new software?
No. Workflows trigger from Slack, email, WhatsApp, web chat, or cron, and write back into the systems your team already uses. The point is to give existing tools superpowers, not to add another dashboard.
How does the engagement structure work?
Four phases over roughly eight weeks: strategy, knowledge layer, build, operate. After launch we either continue as your product team or hand the keys, dashboards, and runbooks over to your engineering org.
Start with one workflow
Bring us the work your team wishes AI could actually finish.
One scoped workflow, eight weeks, in production. We'll tell you on the first call whether it's the right shape — and what we'd do instead if it isn't.