AI Strategy & Enablement

AI Strategy
& Enablement

Unlock the value of AI in your business workflows

We help engineering teams select and deploy AI-powered tools that integrate seamlessly with your existing systems and infrastructure. From context-aware code assistants to AI-driven observability, we enable teams to leverage AI with confidence and consistency.

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Our Approach

AI Strategy
that ships

From opportunity identification to production deployment, we guide you through every step of your AI journey

Identify Opportunities

Identify opportunities to automate, predict, and personalize across your workflows with our comprehensive AI readiness assessment.

Process analysis & mapping
ROI estimation & prioritization
Use case identification

Build AI Roadmaps

Build AI roadmaps aligned with your data, goals, and risk tolerance, ensuring sustainable growth and measurable impact.

Strategic planning & timeline
Resource allocation
Risk assessment & mitigation

Operationalize Models

Select and deploy best-in-breed AI tools and models with confidence, integrating them seamlessly into your existing systems and workflows.

Tool selection & evaluation
Deployment & integration
Monitoring & optimization

AI-Powered Development Tools

Deploy context-aware AI code assistants that understand your infrastructure and deployment patterns. This drives consistency and removes guesswork for development teams.

Infrastructure-aware code generation
Consistent deployment patterns
Safe, accelerated development workflows

Enable Your Teams

Empower your teams with the training, tooling, and best practices needed to leverage AI tools effectively and consistently across your organization.

Team training & workshops
Best practices & frameworks
Ongoing support & mentoring
Knowledge transfer
Tool & platform setup
Governance & compliance
Live Demo

AI in Action

Watch how AI agents interact with your infrastructure through tool calls and MCP integrations

AI Agent - Post-Deployment Check
❯ Check the health of the payment-service we just deployed
Tool Call: read_file
Reading deployment configuration...
target_file: "k8s/payment-service/deployment.yaml"
Tool Call: run_terminal_cmd
Checking pod status in production namespace...
command: "kubectl get pods -n production -l app=payment-service"
✓ Result:
payment-service-7d9f8c5b6-4xm2p Running 0 2m
payment-service-7d9f8c5b6-9kl7n Running 0 2m
payment-service-7d9f8c5b6-xt5r8 Running 0 2m
MCP Call: Datadog
Fetching service latency metrics for last 5 minutes...
server: "datadog"
endpoint: "metrics.query"
query: "avg:trace.http.request.duration{service:payment-service}"
timeframe: "last_5_minutes"
✓ Metrics Retrieved:
47ms
p50 Latency
89ms
p95 Latency
142ms
p99 Latency
• Request Rate: 1,247 req/min
• Error Rate: 0.02%
Tool Call: run_terminal_cmd
Checking recent logs for errors...
command: "kubectl logs -n production -l app=payment-service --tail=50 | grep -i error"
✓ Result:
No critical errors found in recent logs
✓ Deployment Health: Excellent

The payment-service deployment is healthy and performing well:

  • All 3 pods are running successfully
  • Median latency is 47ms (well within 100ms SLA)
  • Error rate is minimal at 0.02%
  • No critical errors in recent logs
  • Request throughput is stable at ~1.2K req/min

✓ Safe to proceed with traffic ramp-up

This demo shows how AI agents use tool calls to interact with your infrastructure and MCP (Model Context Protocol) to fetch real-time metrics from observability platforms like Datadog.

AI that fits your business.
Strategy that ships.

Ready to transform your business with AI? Let's build a strategy that delivers real results.

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