Traditional BI
Explains what happened
- Dashboards and periodic reports
- Structured system data
- Analyst-defined questions
- Manual follow-up
For the operating company
Business Operations Intelligence
Human + AI = 100x
100x is the design ambition, not a result. Impact is measured workflow by workflow against a baseline.
A living intelligence platform that connects relationships, operations, physical assets and AI agents under human authority.
The shift
Business intelligence is becoming operational
Companies adopt AI in three ways: they buy tools, they build new business models, or they redesign how work flows. Nearly everyone has bought the same tools, and few have seen the results they expected. The return is in redesigning the workflow itself.
Traditional BI organizes data into reports. AI adds context, language, prediction and controlled action, allowing intelligence to participate in daily work.
AI-enabled intelligence
Understands what matters now
- Continuous signals and explanations
- Documents, conversations and spatial data
- Role-specific questions in natural language
- Recommended actions with evidence
Business Operations Intelligence
Improves how work gets done
- AI proposes or performs bounded tasks
- Humans retain authority and accountability
- Every result strengthens enterprise memory
What we build
A system of intelligence for the operating company
Sylex.ai creates a living model of the business, then equips each team with governed AI assistants that understand customers, work, assets and decisions.
The missing piece in most AI programs is a company brain: one living memory of what the business does, who it deals with, what it owns and what it decided. Leaders can ask it questions and get answers grounded in the company's own record. It keeps recommending better workflows as it learns. People keep authority over every decision that matters.
01
Ideal customers
Relationship-driven or asset-intensive organizations with valuable decisions trapped across email, CRM, documents and disconnected systems.
02
Entry point
Start with one visible business problem: customer follow-up, pipeline quality, project coordination, operational reporting or facility intelligence.
03
Expansion model
Reuse the same enterprise memory to add new roles, workflows and agent capabilities without rebuilding the foundation.
Products
One enterprise memory, three intelligence products
Each product uses the same governed understanding of the company. Begin with one and extend without rebuilding.
In production
CRM Intelligence
A shared relationship memory that turns every customer interaction into context for the next decision.
Our first product. It runs in production today.
Next module
Operations Intelligence
Connects how work is documented with where work actually happens.
The next module, built on the same enterprise memory.
Pilot stage
Asset and Digital Twin
Anchors equipment, spaces, documents, maintenance and sensor data to a digital twin.
At pilot stage: capture and modelling of spaces, equipment and physical operations.
Built and proven first inside our own operating businesses.
In production
Built for Laser Scan Chicago
Laser Scan Chicago is a 3D laser scanning and scan-to-BIM firm. It is the company our co-founder Sergei Safonov runs, so CRM Intelligence was built with its operator in the room and runs there today.
Every channel in one place
Company mailboxes, voicemail and website enquiries, pulled in every five minutes.
One relationship memory
Contacts, organisations and projects with a single interaction history.
Proposals with approval
Generated from a fixed rate card, with a human approval step before anything is sent.
Authority enforced in the database
Outbound actions are gated on review, not just in the prompt.
Human authority
AI performs bounded work. People keep the authority.
AI proposes or performs bounded tasks. Humans retain authority and accountability. Every result strengthens enterprise memory.
Governance travels through every layer: identity, permissions, source traceability, approval thresholds and audit history.
Identity and permissions
Right person, right reach
Each assistant acts within the role and permissions of the people it serves.
Source traceability
Every answer has a source
Recommendations carry the evidence they came from, so people can check before they act.
Approval thresholds
People approve what matters
People approve communications, pricing, commitments and sensitive customer actions.
Audit history
A record of every decision
What was proposed, who approved it and what happened next stays in the company's memory.
Operating philosophy
Human + AI = 100x
100x is the design ambition: multiply the reach of capable people while preserving judgment, trust and accountability.
01
Shared memory
Teams stop reconstructing context for every decision.
02
Specialist agents
Each expert can supervise more analysis, coordination and follow-through.
03
Continuous awareness
The platform watches for changes between meetings and reporting cycles.
04
Human control
Authority remains explicit for risk, commitments and exceptions.
Actual impact must be measured workflow by workflow against a baseline.
Culture follows process. Change how the work flows and behaviour follows, then judge the result by business outcomes against that baseline, not by how much AI gets used.
Who it is for
High-value decisions with fragmented context
Sylex.ai enters where relationship memory and operational reality must come together.
Professional services
Pursuits, proposals, scope, staffing, project decisions and client follow-up.
Industrial operations
Facility records, equipment, work orders, capacity, production changes and compliance evidence.
Real estate and hospitality
Development decisions, vendors, assets, customer journeys and property operations.
Multi-location businesses
Local performance, service quality, shared standards and cross-site coordination.
Illustrative 12-week pilot
One workflow proves the operating model
A focused pilot creates the first enterprise memory, deploys a role-specific assistant and measures business value before broader rollout.
-
Weeks 1-2
Baseline
Select one workflow, map decisions, define controls and establish current performance.
-
Weeks 3-5
Connect
Integrate source systems and build the first relationship or operational graph.
-
Weeks 6-9
Operate
Launch the copilot and governed agents with a small group of accountable users.
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Weeks 10-12
Prove
Compare results with baseline, capture feedback and define the expansion roadmap.
Pilot scorecard
Decision cycle time Hours reclaimed Response time Conversion or retention Exceptions caught User adoptionFounders
An operator and an engineer
Sylex.ai pairs decades of running real projects with the engineering of memory, agents and governance.
Co-Founder
Sergei Safonov
AIA. Licensed architect, more than 25 years of practice.
He brings the operator's view: how real companies win work, run projects and manage physical assets.
Co-Founder
Alex Safonov
AI and software engineer. BS and MS in Computer Science, Purdue University.
He designs the memory, agents and governance layer.
The Sylex.ai proposition
Build the first 100x workflow
Begin with a decision that matters. Connect the missing context. Give the responsible team an AI partner. Measure the difference. Then scale the operating memory across the company.
Human judgment. AI-scale execution. Compounding business intelligence.