Contact Us

Imagine What’s Next — Secure AI Outcomes on Your Terms

Most AI pilots stall before they produce real value. Tekscape and Cisco help leaders move from ideas to outcomes in about six weeks, on their data. We surface high-value opportunities, design a compliance-native architecture in which only prompts and parameters move and source data stays in its zone, and stand up a measurable pilot. Runs where your data lives. Policy-checked runs. Audit-ready.

Book Your Free Technical Assessment Session
hands holding a security icon with AI written on that
bg dot
cartoon of a person and infographics

Why so many AI pilots stall

  • Compliance and risk. Sensitive data cannot freely move to public endpoints. Governance is often added late instead of designed in.
  • ROI uncertainty. Teams do not define measurable business outcomes, baseline metrics, or payback windows.
  • Skills and ownership gaps. No single owner across data, security, and business value; fragmented efforts stall.
  • Integration friction. Models cannot reach the systems where work happens, so value never shows up in daily tools.
  • Stakeholder alignment. Sponsors, security, and operations are not aligned on guardrails, responsibilities, and acceptance criteria.

What changes the outcome: start from a single workflow, keep data in place, enforce policy at request time, define ROI and acceptance up front, and integrate where your people already work.

Start with one workflow. Leave with a practical plan.

Pick one high-value, low-risk workflow and prove it end-to-end under your guardrails. In a focused engagement, Tekscape identifies where value is hiding, designs a compliance-native path that keeps data in its zone, and produces a six-week pilot plan you can execute immediately with clear owners, KPIs, and safeguards.

What this includes

  • A prioritized short list of five to seven use cases, with
    one selected for the pilot
  • KPI targets and acceptance criteria tied to business
    outcomes
  • A compliance-native architecture sketch using Cisco AI
    Pods; only prompts and parameters move
  • Policy gates defined before execution, with audit trail
    requirements
  • Integration points for Microsoft 365 and core systems
    such as EHR, ERP, PLM, or CRM
  • A week-by-week pilot plan covering build, test, and
    value readout

What you leave with

  • Opportunity Map and ROI snapshot before commitment
  • Risk and Readiness Scorecards across data,
    integration, security, and governance
  • A six-week pilot plan, resourcing guidance, and next-
    step options: Tekscape-led, co-build, or enable your
    team

Guardrails by default

Overlay

Data remains in its zone on-premises, at the edge, or in private cloud​

Overlay 1

Policy checks before every run and end-to-end auditability

Overlay 2

Model choice flexibility, cost visibility, and right-sizing recommendations

The step-by-step path to outcomes in about six weeks

Week 1

Assess

  • Stakeholder workshop to define the business outcome, constraints, and acceptance criteria
  • Light-touch data discovery, data classification, and risk notes
  • Use-case scoring by value, feasibility, and governance complexity
  • Baseline measurement: current cycle time, quality, error rates, or risk costs

Week 2

Design

  • Compliance-native architecture: where code runs, where data remains, which policies gate requests
  • Model options and guardrails: latency, cost, and quality considerations
  • Integration design for Microsoft Teams, Word, SharePoint, Outlook, and target systems such as EHR or ERP
  • Security controls: identity, RBAC, network segmentation, DLP, SIEM or SOAR mappings

Week 3

Build

  • Provision or connect Cisco AI Pods and required compute
  • Implement connectors, prompt harnesses, and evaluation scaffolding
  • Encode policy checks at request time with logging and redaction rules
  • Draft the human-in-the-loop review step and playbooks

Week 4

Integrate
and UAT

  • Wire to Teams and Word for approvals and redlines; wire to system queues for safe handoffs
  • Red-team for prompt safety and data exfiltration
  • Tune prompts, thresholds, and cost controls; finalize acceptance tests

Week 5

Pilot run
and measure

  • Run against real workloads with business owners
  • Capture KPI deltas, reviewer effort, latency, stability, and run costs
  • Collect operator feedback and exceptions for improvement

Week 6

Value readout
and scaling

  • Executive readout with ROI snapshot, risks addressed, and next steps
  • Productionization backlog with clear ownership, SLAs, and monitoring plan
  • Options to scale to adjacent workflows or extend to additional sites

Architecture

  • Private AI runs where data resides

    Your sensitive datasets remain in their existing zones, whether on-premises, at the edge, or in a private cloud network. The system moves only prompts, parameters, and scoped outputs to where they need to go. This avoids risky data lifts to public endpoints and honors regulatory and contractual constraints.
  • Cisco under the hood

    Cisco UCS X-Series with NVIDIA GPU acceleration provides training and inference capacity. Cisco Nexus fabrics support high-speed east-west traffic. Cisco Intersight delivers lifecycle management, telemetry, and compliance at scale. The result is a trusted, observable platform that can live on-premises, in a private cloud, or at the edge.
  • Policy checks before every run

    Policy is enforced at request time, not after the fact. Identity, role, scope, data-class rules, and redaction are applied before the model runs. Prompts, parameters, and outputs are logged for an end-to-end audit trail. Governance is built into each inference.
  • Where the value shows up

    Teams cards for alerts, approvals, and case files. Word documents for redlines and summaries. SharePoint and Outlook for content and workflow. Connectors to your core systems such as EHR, ERP, PLM, or CRM so outcomes appear in the tools people already use.

DIY path: how to do this yourself

professional meeting in the office

SVG 11 A. Team and ownership

  • Executive sponsor to clear obstacles and fund
  • Product manager to own the outcome and acceptance
  • Data steward for classification and access
  • Security lead for controls and audit
  • Integration engineer for Teams/Word/system connections
  • Business owner to validate value

SVG 11B. Use-case selection rubric

  • Score 1–5 for: business value (revenue/cost/risk)
  • Technical feasibility
  • Data availability and quality
  • Regulatory risk
  • Integration effort
  • Sponsor commitment
  • Prioritize high-value, low-risk use cases with clear ownership

SVG 11C. Data and governance checklist

  • Data classification (public/internal/confidential/restricted)
  • Residency requirements (on-prem/edge/cloud)
  • Where code runs vs. where data stays
  • Redaction and masking rules
  • Pre-run policy checks with logging
  • Human-in-the-loop review steps
  • Audit trail requirements
woman using a laptop

SVG 11D. Measurement plan

  • Document baseline: current cycle time, cost per transaction, error rates, quality scores
  • Choose 2–4 KPIs that matter to sponsors
  • Set acceptance thresholds
  • Define sample size and measurement period
  • Plan weekly readouts during pilot

SVG 11E. Build scaffolding

  • Prompt harness with version control
  • Test sets (positive and negative cases)
  • Cost controls and usage limits
  • Evaluation dashboards for quality, latency, and cost
  • Logging infrastructure for audit and debugging

SVG 11F. Integration Plan

  • Start with one user surface (Teams card or Word draft) for feedback
  • One system handoff (EHR queue, ERP record, or CRM note)
  • Keep integration thin and testable
  • Validate end-to-end with real users before scaling
professional meeting in the office

SVG 11G. Go-live and learning loop

  • Run daily for two weeks with business owners
  • Weekly readout on KPIs, exceptions, and feedback
  • Tune prompts and thresholds based on real usage
  • Decide at week six: productionize with full SLAs, pivot to adjacent workflow, or stop and apply learnings

Proof scenarios

FINANCIAL SERVICES
AML alerts you can explain

AI triages AML alerts within your PCI zone. Every request is policy-checked, and an explainable case opens in Teams with source-linked evidence. Draft suspicious activity report language in Word while raw data remains in your environment. Impact: fewer false positives, faster investigations, and a stronger audit posture.

HEALTHCARE
Chart summaries without PHI sprawl

Notes and labs are processed inside your HIPAA zone. Structured outputs post to EHR queues for clinician sign-off. Every run is policy-checked and audit-ready. Clinicians use a Teams card to accept or modify. No PHI leaves your protected boundary. Impact: less charting time, faster prior authorization and discharge, defensible PHI governance.

MEDIA AND ENTERTAINMENT
Sense audience momentum early

Licensed feeds and first-party analytics fuse into a momentum signal. Teams sends a notification at threshold with a one-slide dossier for producers. No raw feeds leak; requests are logged. Impact: shorter scouting cycles, smarter pre-emptive offers, and confidential strategy.

CORPORATE DEVELOPMENT AND PRIVATE EQUITY
Dossier to draft LOI

Agents assemble a living dossier from SharePoint, Outlook, and ERP. Draft letters of intent in Word inside Teams for redlines. All activity stays in your environment with policy checks before every run and a full audit trail. Impact: faster sourcing, accelerated diligence, and first drafts minutes after a signal spike.

MANUFACTURING
Vision and time-series at the edge

Run vision and time-series models near OT systems so trade secrets stay on-site. Policychecked inference opens a case with annotated frames, lot history, and a recommended action in Teams. Summaries export to Word for quality assurance sign-off. Impact: fewer escapes, lower scrap, earlier detection, and full traceability.

ROI method and value readout

Before you build

  • Quantify the problem: who is affected, how often, and what it costs
  • Define KPIs and acceptance thresholds that a business owner will recognize
  • Document the baseline with time-stamped samples or system logs

During the pilot

  • Measure cycle time, accuracy or quality, reviewer effort, and run cost
  • Track exceptions and categorize by root cause
  • Record human-in-the-loop touches to identify automation potential

At readout

  • Show KPI deltas with sample counts and confidence bounds
  • Present a payback model with sensitivity ranges
  • Document risks addressed, residual risks, and the productionization backlog

Governance and reassurance

person using a laptop

Data sovereignty.

Data remains in its zone on-premises, at the edge, or in a private cloud network.

Pre-run policy checks.

Identity, role, scope, and data-class rules are enforced before execution.

Auditability.

Prompts, parameters, model calls, and outputs are logged end-to-end with human-in-the-loop events.

Security controls.

Integrates with identity, network controls, DLP, EDR or XDR, and SIEM or SOAR.

Model choice.

Bring your preferred model, open or proprietary, including edge or private endpoints.

Cost transparency.

Usage guardrails, per-use cost views, and right-sizing recommendations.

Readiness checklist

One sponsor + one workflow owner

Single workflow with clear value

Data classification/residency documented

Policy gates & audit needs defined

Baseline & acceptance thresholds set

Minimum environment for private runs available or planned

Integration targets identified (one user surface + one system handoff)​

Common questions

Do we have to move sensitive data to use your AI?

No. Only prompts and parameters move. Source data remains in its zone. Outcomes are scoped and logged.

Yes. We use Teams for approvals and alerts, Word for redlines and summaries, and SharePoint and Outlook for content and workflow inside your tenant.

A zero-trust posture with fine-grained role-based access and federated identity, request-time policy checks before every run, encrypted transport, and complete audit trails to your SOC and NOC.

Your choice. Open or proprietary models deployed privately or via approved private endpoints, selected for fit, latency, quality, and cost.

We model time savings, throughput lift, quality improvements, and avoided rework or risk costs, show ranges and assumptions, and validate during the pilot.

No. Start with one workflow that shows measurable value while guardrails are implemented. Infrastructure strategy follows the proven outcome.

Begin where it is fastest. As sensitivity or scale grows, Cisco AI Pods with Tekscape orchestration provide a path to repatriate for cost and control without changing the outcome model.

Why Tekscape and partner logo blue 1

Outcome-first

Start from a business result, not a tool wish list.

Data-sovereign by design

Only prompts and parameters move; source data stays in its zone.

Governance in-run

Policy checks before every run and complete audit trails.

Six-week path

A structured pilot that proves value and safety quickly.

Fit to your stack

Microsoft 365 integration and Cisco reference architectures under the hood.

Boutique depth, national reach

Senior architects with on-site delivery where you operate.