response SLA
OTOworks Engine
Enterprise knowledge, built into automation
We connect documents, messages, operational data, and approval logic into one engine so people touch less of the workflow. This is not packaged software. It is a custom build shaped around how your operations actually run.
average PoC lead time
build per domain
OTOworks Engine
Incoming work signals
Policies, SOPs, and approval criteria
Messages, approvals, email, and frontline requests
Operational data, exception cases, and human judgment
Production outcomes
Decision-ready automation flows
Execution logic with governance built in
Operational rollout with monitoring and handoff in mind
Real Client Case Studies
Actual automation projects we've shipped with clients
Process Automation
Content Marketing
Plenty of tools, but content still never left human hands
Ad platforms, CRM, analytics, AI writing assistants. The marketing stack was full. The team still kept getting stuck in the same place: content. Topics got picked on gut feel. Writing standards drifted between people. Every platform needed manual reformatting. Adding draft automation didn't fix it. It just shifted the work. Someone still had to rewrite the AI output line by line, and the automation that was supposed to save time quietly created a new review workload.
Half a day → 30 min
Time per content piece
Avg 7 → 1
Draft revision rounds
Process Automation
Legal Services
Phone-only consultations, case management by memory alone
After leaving a law firm to open a solo practice, the lawyer ran into a different kind of problem. Handling cases well and bringing cases in were not the same thing at all. Consultation requests came only by phone, and the history was scattered across notepads and memory. Miss one call, lose a potential client. From the client's side, calling a lawyer in the first place felt like a hurdle. And not every consultation turned into a case. Simple questions were taking up the same time as serious matters, which left less room for the work that really mattered.
Business hours → 24/7
Intake Availability
0
Missed Consultations
Process Automation
Food & Beverage Distribution
Reading purchase order documents by eye and typing into Excel — a mid-sized manufacturer's daily routine
This company processes purchase and shipment orders through their internal groupware system. The core task was finding approved shipment documents from other teams, extracting data, and entering it into vendor-specific Excel templates for ERP upload. The problem: this entire process was 100% manual. Worse, the shipment request documents were embedded as images, making copy-paste impossible. Each vendor required different data fields and Excel formats, and some values like pricing had to be manually calculated.
1h × vendors → 1min
Manual Work Time
0 cases
Data Entry Errors
AI infrastructure that understands your work
What is OTOworks Engine
As work inputs are turned into automation logic, we model relationships, rules, and operating context together. The point is not just document retrieval, but bringing the way work actually runs into the system.
01
Relationship-aware AI
Not just document search. We map the relationships and context across your work, and a domain knowledge graph grounds every AI decision.
02
Custom build
This is not an open-source SaaS. We analyze your processes, documents, and systems to build a dedicated ontology and RAG pipeline for you.
03
Running in production
Shipping is not the end. We wire it into real operations, collect field feedback, and improve the system with you over time.
Enterprise Engagement Process
A continuous path from diagnosis to operating rollout
- 1
01
1–2 weeks
Discovery
On-site discovery to understand workflows and data sources. We pinpoint where automation creates the most value.
- 2
02
4–8 weeks
PoC Design & Build
We pick one core workflow and build the ontology and RAG pipeline against real data.
- 3
03
3–6 months
Production Build
We expand the PoC into a full custom build, integrate with existing systems, and establish governance.
- 4
04
Ongoing
Operate & Improve
After go-live we monitor, tune, and extend with you. Field feedback flows back into the ontology.
Relationships and rules modeled together
OTOworks Engine combines RAG (Retrieval-Augmented Generation), GraphRAG (graph-based reasoning over knowledge), and domain-specific ontologies (knowledge structuring). For each customer we tailor these layers to your data, workflows, and policies.
Technology Stack
Relationships and rules modeled together
RAG pipeline
Real-time retrieval from your unstructured documents, chat, and records, fed as grounding for AI decisions.
GraphRAG
Reasoning over relationships, not just documents. Handles multi-hop questions across entities.
Domain ontology
A knowledge graph of your business concepts, relations, and policies. This is the ground truth for AI judgment.
Honest capacity signal
Because every engagement is custom-built, we keep concurrent project slots limited. We prioritize teams with a clearly defined high-value workflow, respond within one business day, and share the next available start window immediately.
Let's scope your custom build
We start by understanding the operation, not by walking through a product demo. In a 30-minute call we can map the first automation target and the likely PoC scope together.
Request Enterprise ConsultationContact Us · Email: contract@otoworks.ai