Start with the outcome, not the model.
Many valuable AI projects first need reliable data, sound integration, and a named owner. We confirm those foundations before recommending a model or tool.
AI readiness · Integration · Analytics · Governed automation
Practical AI. Secure integration. Measurable outcomes.
Terza helps organizations assess AI opportunities, connect enterprise systems, and implement governed analytics and automation — with training and support from planning through operations.
- Who we serve
- Commercial enterprises, Department of Defense organizations, higher education, and public-sector agencies.
- Problems we address
- Disconnected data, unclear AI priorities, manual reporting, and workflows that need to scale safely.
- What you can buy
- Readiness assessments, scoped implementations, advisory hours, training, and ongoing support.
- Security first
- Built by a team with a DoD mission-critical systems background — least privilege, human approval, and reviewable audit records.
01 / How we think about AI
The right design for the problem — not the most complex one.
Fit the architecture to the task.
A well-evaluated single model, a deterministic workflow, a retrieval assistant, or a multi-agent system — we select models, workflows, and agent patterns based on the task, its risk, and its operating cost.
Automation with ownership.
Automated steps have defined limits, a responsible owner, human review for consequential actions, and a clear path to escalate when something falls outside policy.
02 / Services
Six services you can engage separately or together.
Each service is scoped to your systems, data, and goals. The deliverables below are typical examples; the final set is agreed with you before work begins.
S-01
AI Readiness & Strategy
For organizations that see AI potential but lack a clear, prioritized, fundable plan.
Typical deliverables
- System inventory and data-readiness review
- Prioritized use cases with owners and constraints
- Business case and implementation roadmap
Evaluated by Stakeholder agreement on priorities, measures, and a roadmap your team can fund and act on.
S-02
Data Integration & AI Implementation
For AI initiatives that depend on data spread across records, finance, CRM, case-management, or operational systems.
Typical deliverables
- Source-system assessment and data mapping
- Approved connections using each system's supported interfaces
- Access controls and operational documentation
Evaluated by Data arriving complete, on schedule, and only to authorized users — verified against agreed test cases.
S-03
Predictive Analytics & Decision Support
For teams that need earlier signals — on program outcomes, applications, caseloads, budgets, or spending — to act in time.
Typical deliverables
- Agreed metrics and data-quality checks
- Dashboards and reporting for defined audiences
- Forecasting or predictive-model evaluation, plus decision-support workflows
Evaluated by Model performance and bias checks on held-out data, and whether the people using the outputs find them timely and actionable.
S-04
AI Assistants & Workflow Automation
For repetitive, document-heavy, or multi-step work — including agentic and multi-agent workflows where a task genuinely needs coordination across tools.
Typical deliverables
- Bounded workflows with a defined list of permitted tools
- Approval gates and exception handling
- Audit records for actions taken
Evaluated by Task quality against an agreed test set, correct escalation of exceptions, and time saved as measured by your team.
S-05
AI Security, Privacy & Governance
For security, privacy, and risk teams who need to understand and approve how an AI system handles data and decisions.
Typical deliverables
- Data-flow diagrams and control mapping
- Risk register and evaluation approach (accuracy, bias, appropriate use)
- Defined operating responsibilities
Evaluated by Your reviewers having the evidence they need to approve, condition, or decline a system.
S-06
Training, Adoption & Ongoing Support
For organizations that want staff to use AI tools well and own them long after launch.
Typical deliverables
- Role-specific training and administrator enablement
- Change-management support and runbooks
- Support and improvement arrangements
Evaluated by Staff completing agreed tasks independently, and issues resolved within the support terms you agree with us.
03 / Education & public sector
Built for institutions with complex systems and public accountability.
Universities and public agencies run records, case-management, finance, procurement, and CRM systems that each have their own owners and rules. We help connect them, add analytics and assistance where they help, and document how data and decisions are governed.
- Early-warning analytics and intervention tracking
- Application, intake, and case-tracking workflows
- Budget forecasting and spend analysis
- Governed knowledge assistants for staff and the people they serve
- Staff training and ongoing technical support
04 / Approach
Five stages, each with an agreed acceptance check.
We move to the next stage only when the current one meets criteria you have agreed with us. Durations depend on scope, data readiness, and your review cycles, and are set during Discover.
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01
Discover
Agree on outcomes, owners, existing systems, data readiness, and priority use cases.
Outputs Outcome statement, system and data inventory, prioritized use-case list.
Acceptance check Sponsors and system owners confirm priorities, measures, and constraints.
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02
Architect
Define integrations, data flows, access controls, deployment approach, evaluation plan, and responsibilities.
Outputs Architecture and data-flow diagrams, control mapping, evaluation plan, responsibility matrix.
Acceptance check IT, security, and data owners review and approve the design before build begins.
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03
Pilot
Implement one bounded use case with agreed measures and feedback from real users.
Outputs Working pilot, evaluation results, user-feedback summary.
Acceptance check Results are reviewed against the agreed measures; the decision to expand, adjust, or stop is documented.
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04
Expand
Introduce additional workflows after reviewing pilot results and operational readiness.
Outputs Prioritized expansion plan, updated controls and documentation.
Acceptance check Each new workflow passes the same review and evaluation gates as the pilot.
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05
Operate & Enable
Provide monitoring, support, training, runbooks, and handover where appropriate.
Outputs Runbooks, monitoring, trained administrators, support arrangement.
Acceptance check Your team can operate, change, and audit the system with the handed-over material — or an agreed support arrangement is in place.
Built into every stage
- Change management — communication and process updates for the people affected.
- Data preparation — quality checks and fixes before anything depends on the data.
- Staff training — role-specific, not a single generic session.
- Long-term ownership — named owners and documentation for everything we build.
05 / Security & governance
Security-first AI for an escalating threat landscape.
As frontier AI models grow more capable, so do the threats against the systems that use them — and so do adversaries who use AI themselves. Our team brings the discipline of DoD mission-critical systems engineering to every engagement: threat modeling first, least privilege throughout, and controls your security, privacy, and risk teams can verify.
What we design against
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Prompt injection and manipulated content
Instructions hidden in documents, emails, or web pages that try to redirect an assistant. We isolate untrusted content and limit what any single model output can trigger.
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Data leakage through AI tools
Sensitive data exposed through prompts, retrieval, logs, or third-party providers. We enforce data classification, permission-aware retrieval, and approved-provider controls.
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Over-privileged agents
Assistants holding more access than the task requires. We scope tools and credentials to each workflow and gate consequential actions behind human approval.
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AI-accelerated attacks
Adversaries using frontier models to find vulnerabilities and craft convincing phishing faster. We red-team AI systems before release and design monitoring and incident response for that pace.
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Model and supply-chain risk
Unvetted models, plugins, and dependencies. We review providers, pin and verify components, and document the provenance of what runs in production.
Controls designed and agreed for each engagement
Security and privacy are design inputs, not a final checklist. These controls are adapted to your policies, systems, and classification requirements.
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Data classification and approved uses
Which data an AI system may use, for what purpose, and which data is excluded — agreed with data owners up front.
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Institutional identity and least-privilege access
Users sign in through your identity provider. Integrations receive only the permissions they need, using the authentication methods each source system supports.
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Human review for consequential actions
Actions that affect people, money, or records go through an approval gate. Where an action can be undone we design rollback; where it cannot, we document recovery procedures.
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Privacy-aware logging and retention
Audit records capture what is needed to explain and investigate activity, with sensitive data redacted, access restricted, and retention periods you set.
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Deployment location and provider controls
Where systems run, which model providers are approved, and the contractual and technical terms for how providers handle your data.
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Evaluation for accuracy, bias, and appropriate use
Agreed test sets and review before release, with results explained in language decision-makers can act on.
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Monitoring, incident response, and data return
Ongoing monitoring, an agreed incident process, and data return or deletion at the end of an engagement.
- RequestA staff member asks an assistant to prepare an action.
- DraftThe assistant prepares it using only permitted data and tools.
- Policy checkRules decide whether the action can proceed, needs review, or is blocked.
- Human reviewAn authorized person approves, edits, or rejects.
- ExecuteOnly approved actions run, through scoped credentials.
- RecordAn audit record notes who approved what, and when.
Technical example: an illustrative tool policy
workflow: invoice-reconciliation # illustrative
tools:
erp.read_invoices: allow
erp.post_journal: require_approval(amount > agreed_limit)
email.send: deny
credentials: scoped to this workflow
audit: actions + approvals, sensitive fields redacted
retention: set by data owner
release_gate: agreed evaluation criteria metWe can align our work with frameworks such as the NIST AI Risk Management Framework and your institution's own policies. Framework alignment describes our approach; it is not a certification, attestation, or authorization.
06 / Engagement options
Flexible ways to work with us.
Start small or engage end to end. Pricing reflects your scope, systems, data readiness, and support needs, so we provide a proposal after a short scoping conversation.
Readiness assessment
A defined assessment with a fixed scope and a written roadmap at the end.
Implementation
Delivery of an agreed use case through pilot and into production, at a fixed fee where scope allows.
Advisory & consulting
Architects, engineers, and analysts for defined pieces of work.
Training
Role-specific sessions for leaders, staff, and administrators.
Ongoing support & improvement
Monitoring, maintenance, and enhancements under agreed support terms.
How engagements start
- Discuss scope. A conversation about your goals, systems, and constraints.
- Confirm requirements. We document what's in and out of scope, plus dependencies on your team.
- Agree deliverables and pricing. A written proposal through the purchasing process your organization uses.
- Implement and support. Delivery against the agreed acceptance checks, then the support arrangement you choose.
07 / Company
Security engineers first. AI second.
Terza's team designed and implemented mission-critical systems for the U.S. Department of Defense, built with the highest level of security in mind. We apply that same rigor — threat modeling, least-privilege access, documented interfaces, and verifiable controls — to AI readiness, integration, analytics, and automation.
- Commercial enterprises
- Secure AI integration with ERP, CRM, data platforms, and internal systems.
- Department of Defense
- AI capabilities engineered for mission-critical environments and stringent security requirements.
- Education & public sector
- Governed analytics and assistance for institutions with public accountability. Learn more →
Integrate before you automate.
AI is only as useful as the data and systems it can reliably reach. We start with the plumbing.
Make important actions traceable.
If an action can't be explained and reviewed, it can't be trusted — or debugged.
Choose the simplest design that works.
Narrow responsibilities, typed interfaces, and testable behavior — with agents only where they earn their complexity.
Security from the first sketch.
Threat models, access design, and audit requirements are inputs to the architecture, not a checklist at the end. That's how mission-critical defense systems are built, and it's how we build AI.
08 / Contact
Discuss your AI initiative.
Tell us about the outcome you're working toward and the systems involved. A member of our team will reply by email to arrange an initial conversation.
Please don't include personal records (such as student, patient, or customer records), passwords or other credentials, or other sensitive personal information in this form. See our privacy notice for how we handle what you send.