Our approach
Federated operational intelligence
Stream Solve learns how telecom operations actually run, from system data, domain expertise and the everyday decisions of your teams, and turns it into evidence-backed recommendations that your experts approve.
Our principle
Learn how operations really run
Data models and schemas describe how operations are supposed to work. We also learn how they actually work: we start with system data, logs, alarms and events, add interpretation across domains, and then learn from authorized observation of workflows, human decisions and their outcomes.
People stay in charge
- Agents start read-only and advisory
- Domain experts accept, revise or reject every recommendation
- Every recommendation comes with its evidence
- Nothing changes in your network or systems silently
Three sources of intelligence
What the agents learn from
Each source covers what the others miss.
System intelligence
Entity models, topology, inventories, fault and performance telemetry, tickets, product and customer records, change histories, events and API contracts, with identity, timestamps, provenance and meaning normalized across domains.
Federated domain intelligence
Each domain agent keeps its own vocabulary, knowledge base, policies and evidence. Agents exchange scoped questions, observations, hypotheses, confidence and constraints, never unrestricted raw records or direct database access.
Human operational intelligence
With notice and authorization, the steps experts take: navigation, investigation, filters, decisions, escalations and overrides, correlated with tickets and outcomes. Credentials, unnecessary personal data and sensitive screen content are left out.
Learning and approval lifecycle
From observation to approved playbook
Eight steps, with people in control of every change.
Ingest & map
Build domain ontologies and evidence links from structured data, logs and documentation.
Observe
Collect permitted workflow events and human decisions, with business context and audit trails.
Discover
Find recurring patterns, cross-domain dependencies and candidate improvements, and separate correlation from causation.
Converse
Ask other domain agents for bounded, policy-compliant explanations and corroborating evidence.
Recommend
Write a plain-language proposal with evidence, expected benefits, assumptions, risks and rollback conditions.
Review
Domain owners approve, modify or reject. Cross-domain changes need every relevant approval.
Codify
Publish approved rules and playbooks as versioned, testable artifacts, not as uncontrolled model updates.
Evaluate
Monitor drift, false positives, outcomes and policy compliance, and revise or withdraw what does not work.
Trust and governance
Controls built in from day one
Read-only first
Pilots use read-only connectors and workflow instrumentation, with no silent automated provisioning.
Notice and least privilege
Explicit user notice, role-based access, data minimization, retention limits and a security review with the operator.
Human sign-off
Every operational recommendation needs approval, scoped by domain, risk and expiry.
Evidence and provenance
Machine-readable evidence for each claim, and agent conversations that can be traced end to end.
Replay before execution
Offline replay and shadow-mode evaluation come before any controlled execution.
Policy stays in charge
Model outputs cannot override approved policy or security controls, and nothing rewrites policy silently.
Industry-neutral by design
One core, industry packs on top
The core works the same way in every industry. Industry packs add the domain knowledge and the policy ownership, starting with telecom.
Core
Learns from structured models and logs, federated domain observations, authorized workflow behavior and outcomes. It runs the agents, the evidence engine and the approval workflows.
Industry packs
Domain-specific semantic mapping and policy ownership. Our telecom pack follows TM Forum-aligned processes and information models, without hard-coding telecom assumptions into the core.
Business systems
DNext, our flagship, provides the OSS/BSS services and transaction execution. The agents connect to DNext and to your existing systems through documented interfaces.
Explore DNextIllustrative use cases
What it looks like in practice
5G fixed wireless degradation
A fault agent notices recurring loss of preferred mid-band connectivity. Before proposing a band change, it asks the performance and capacity agents whether congestion or time-of-day patterns explain the symptoms. It correlates customer complaints, cell utilization and earlier fixes, then recommends a bounded configuration or resource trial for the domain owner to approve.
Network conditions are verified first. Correlation alone is never treated as the cause.
Capacity upgrades into new offers
After a planned cell-site upgrade, planning and capacity agents find where tested service headroom has improved. Product, qualification, security and marketing agents turn the technical change into feasible offers, eligibility rules and campaigns.
Commercial rollout waits for verified capacity, coverage, quality, security and business approvals.
Start small
A focused pilot, measured from day one
We start with one narrow scenario, such as 5G fixed-wireless degradation. Fault and capacity/performance agents work on sample alarms, utilization data, customer trouble tickets and selected, approved workflow events.
You see a cross-domain agent conversation produce an evidence-backed recommendation and route it to your team for review.
What we measure
- Explanation accuracy
- Recommendation acceptance
- False-positive rate
- Investigation time saved
Put your operations knowledge to work
Tell us which domain needs the most help today. We will propose a narrow, read-only pilot with clear measures of success.