Technology solutions built around the problem you’re solving.
Neotrix begins with the business requirement and determines the technology needed to support it.
Secure Generative AI
Put generative AI where it earns its place — and implement it securely.
An organization sees potential in generative AI but cannot confidently say which of its business problems justify the investment, which processes are suitable, what technology is required, or how to implement it without creating security and data-protection risk.
Starting from the technology rather than the problem tends to produce cost and complexity without producing business value.
Neotrix starts by evaluating the work itself — the processes, the information-intensive tasks, the systems already in place, and the requirements and constraints around them — and identifies where generative AI would make a measurable difference.
Where the business case holds up, Neotrix designs, implements, integrates and supports the solution.
Solutions may include:
- Knowledge assistants
- Document intelligence
- Information retrieval
- Employee support
- AI-assisted workflows
- Generative AI applications
Security, access controls, monitoring, evaluation, data protection, human oversight and governance requirements are treated as part of the solution rather than as a later review step. Data residency and isolation are addressed explicitly in the architecture, and what can be committed to depends on the platform and deployment model selected for that environment.
- AI investment tied to a specific, defined business need
- Faster access to organizational information
- Less repetitive information processing
- Solutions that respect existing permissions and security requirements
- Oversight and evaluation in place from the start
- Retrieval-augmented generation (RAG)
- Identity-aware retrieval and permission trimming
- Model selection and evaluation
- Prompt and context design
- Guardrails and content filtering
- Vector and hybrid search
- AWS and Azure AI services
- Application and API integration
- Logging, tracing and evaluation harnesses
AI Governance & Assurance
Turn AI governance requirements into controls that actually operate.
AI is often in use across an organization before governance catches up. Questions follow about risk, access, data protection, accountability, human oversight, evaluation, monitoring and documentation — and a written policy on its own rarely answers them.
The gap is usually not the policy. It is the absence of operational and technical controls that make the policy observable.
AI governance is both a standalone Neotrix service and a capability built into every Neotrix AI implementation.
Neotrix assesses how AI is being used today, identifies governance gaps, establishes requirements and accountability, and then translates those requirements into practical operational and technical controls — the organizational side and the engineering side of the same problem.
Capabilities include:
- AI readiness assessments
- AI risk assessments
- Governance policies and standards
- Roles and accountability
- Access and authorization controls
- Guardrails
- AI evaluation
- Monitoring
- Human oversight
- Traceability and logging
- Governance documentation
- Audit-ready evidence
- Better visibility into how AI is actually being used
- Clear accountability for AI decisions and systems
- Governance controls applied consistently rather than per team
- Monitoring and evaluation that produce evidence, not assertions
- Documentation and traceability that hold up to review
- Control mapping to recognized AI risk frameworks
- Identity and access integration
- Policy-as-configuration and guardrail enforcement
- Evaluation pipelines and scorecards
- Model and prompt logging
- Monitoring and alerting
- Evidence collection and retention
- Review and approval workflows
Process Automation & Systems Integration
Take the manual handoffs out of the process and connect the systems behind it.
Employees spend a significant share of their week on repetitive tasks, chasing approvals, re-keying information between applications, and running processes through email and spreadsheets because the systems involved do not work together.
The cost shows up as delay, duplicate work, inconsistent information and limited visibility into where anything stands.
Neotrix examines the process as it actually runs before deciding what to automate. Some steps should be removed rather than automated; some belong in a platform the organization already owns.
From there Neotrix improves and automates the process, connects the systems it depends on, and keeps approvals, controls, logging and auditability intact.
This typically addresses:
- Reducing repetitive work
- Improving business processes
- Connecting systems
- Eliminating unnecessary manual handoffs
- Improving information movement
- Improving visibility
- Maintaining approvals and controls
- Logging
- Auditability
- Less duplicate data entry and fewer manual handoffs
- Faster processing and fewer delays
- More consistent information across systems
- Clearer visibility into process status
- Controls and audit trail preserved, not traded away
- Existing commercial platforms
- Workflow and low-code platforms
- APIs and webhooks
- Integration platforms
- Event-driven integration
- Cloud services
- Custom components where justified
- Exception handling and retry design
Cloud Migration & Modernization
Migrate to the cloud and/or modernize existing cloud environments.
An organization needs to move workloads to the cloud, improve an environment it already runs there, or both — and does not have the internal capacity or specialized expertise to assess the environment, choose an architecture, plan the work and execute it safely.
The requirement is usually driven by something concrete: an application that needs to scale, an operational burden that keeps growing, a security requirement, or a contract or facility deadline.
Neotrix supports both directions of this work: migrating applications, infrastructure and workloads to the cloud, and modernizing environments already running there.
That covers current-environment assessment, workload discovery, target architecture, security requirements, migration planning and execution, infrastructure automation, testing and validation, documentation, and transition to whoever will operate it.
- A cloud transition that completes without unplanned disruption
- Improved scalability and reliability
- More consistent, repeatable infrastructure management
- Better operational visibility
- An environment the organization's own team can run
- Cloud architecture
- Infrastructure automation
- Infrastructure as code
- Terraform
- Container platforms
- Kubernetes
- Platform engineering
- DevSecOps
- CI/CD
- Observability
- Security
- Reliability engineering
These are supporting engineering capabilities applied in service of the migration or modernization requirement — not separate offerings to be purchased on their own.
Cloud & Technology Cost Optimization
Find what the spending is actually buying, then put controls around it.
Technology spending keeps rising, but it is difficult to tell how much of it is producing value. Unused resources accumulate, infrastructure is sized for a peak that never arrives, storage grows unattended, and cost is hard to attribute to a team, product or workload.
Without visibility, cost conversations become across-the-board cuts rather than decisions.
Neotrix evaluates utilization, architecture, infrastructure configuration, spending patterns and the operational practices behind them, then separates one-time savings from the structural changes that keep costs from drifting back.
Where AI/ML workloads are part of the environment, their infrastructure and operating costs are evaluated on the same terms.
Areas typically reviewed:
- Identifying unnecessary spending
- Improving resource utilization
- Architecture optimization
- Cost visibility
- Cost controls
- Cloud cost management
- AI/ML infrastructure cost
- Ongoing cost oversight
- Unnecessary spending identified and removed
- Cost attributable to teams, products and workloads
- Better-informed capacity and architecture decisions
- Controls that hold after the engagement ends
- Utilization and rightsizing analysis
- Architecture review
- Storage lifecycle policies
- Tagging and cost allocation
- Budgets, alerts and guardrails
- Commitment and capacity planning
- Automated cleanup of idle resources
- GPU and AI/ML workload cost analysis
Neotrix does not quote a savings percentage before reviewing an environment. The size of the opportunity is a finding, not a promise.
Common questions about these solutions
What does “secure generative AI” mean in practice?
It means the AI solution is designed from the start with access control, data boundaries and oversight built in — answers grounded in your approved content, confidential data kept out of external tools, inputs and outputs logged, and a human approval step before consequential actions.
Do we need an AI governance program before deploying AI?
You need proportionate controls, not a bureaucracy. For most organizations that starts with an inventory of AI in use, a named owner and approval path, and basic guardrails and monitoring. We translate frameworks like the NIST AI Risk Management Framework — and Texas requirements such as SB 1964 — into controls that actually operate.
Which processes are worth automating first?
High-volume, rule-based work with clear inputs — approvals, data re-entry between systems, document intake, onboarding checklists. We look for steps where information already exists digitally but moves by manual handoff, then connect the systems behind them.
Should we migrate to the cloud or modernize what we already run?
They usually go together: migrate what benefits from managed services, modernize what stays. The deciding factors are security posture, reliability requirements and unit cost — not novelty. We assess before we move anything.
How do you reduce cloud and technology costs without hurting operations?
By fixing the causes, not just the symptoms: right-sizing and eliminating idle resources, improving architecture and utilization, renegotiating commitments, and leaving behind cost visibility, budgets and alerts so the savings persist.
Not sure which of these fits?
Describe the problem and we’ll tell you which approach is realistic — including when the answer is a platform you already own.