AI Practice
Evolve from Digital‑Native to AI‑Native
We embed Generative AI into the fabric of delivery—turning concepts into shippable software with measurable gains in predictability, velocity, and quality. Our four pillars combine consulting rigor with production‑ready accelerators.
30-45%
Estimation variance
(first 90 days)
1.6-2.2x
Dev thruput acceleration
-25-40%
Escaped defects/quality uplift
Data & AI Foundations
Build the bedrock for AI at scale—reliable data, observable models, and enforceable safety. Designed to integrate with your existing cloud and security posture.
Core capabilities
Data readiness: ingestion, lineage, quality rules, and semantic catalogues. Model lifecycle: evaluation, drift detection, feedback loops, and rollback plans.
Deliverables
Reference architecture & landing zone. Guardrail policies & playbooks. Operational dashboards & runbooks. Integration blueprints for cloud & enterprise tools.
Quality & compliance
Structured data & model SLOs. Audit‑ready logs & review trails. Usage caps & per‑case costing.
SDLC Intelligence
Transform requirements into predictable delivery. Our tools consume concepts, emails, PDFs and meeting notes to generate Epics → Features → Stories, derive story‑level WBS, and recommend the optimal execution mode for each task with calibrated effort estimates.
What it does
- Parses unstructured inputs and produces structured backlogs with acceptance criteria and dependency maps.
- Generates WBS per story: design, implementation, tests, review, and release tasks.
- Classifies tasks into Manual, Vibe Coding (AI‑assisted), or Cloud/Background Agent execution.
- Outputs calibrated effort ranges by execution mode with confidence bands and risk flags.
Business impact
- Reduce planning cycles from weeks to days
- 30–45% lower estimation variance
- 1.6–2.2× developer throughput
- Improved predictability & fewer rollovers
AI Adoption Journey
Practical, outcome‑driven adoption that balances innovation with governance. We design the roadmap, set the guardrails, and enable teams to scale GenAI safely across functions.
Framework
Maturity assessment across strategy, data, delivery, risk, and value tracking. Use‑case portfolio with value vs. feasibility scoring and staged rollouts.
Outcomes
Clear 6–12 month roadmap with investment cases and KPIs. Reduced compliance risk with embedded guardrails. Faster time‑to‑value via prioritized pilots.
Adoption metrics
3–5 pilots → 2 scaled use cases / quarter. Policy‑aligned prompts, logging, RBAC. Role‑based enablement for 6+ personas.
Innovation Sprint Factory
From vision to MVP in weeks. We blend discovery, design, and AI‑assisted build with lean experimentation—then harden the solution for enterprise scale.
Method
- Discovery & framing: problem definition, success metrics, risks.
- AI‑assisted prototypes: UX flows, data mocks, and working spikes.
- MVP build: code generation, test scaffolds, CI/CD and observability baked in.
- Validation: user tests, A/Bs, telemetry‑driven iteration.
- Handover: documentation, runbooks, backlog and scale plan.
What you get
- Clickable prototype in 5–10 days
- MVP in 4–8 weeks, cloud‑ready
- Landing page + demo assets for stakeholders
- Path to production (security, data, ops)
Ready to Build AI the Right Way?
Let us help you design, build, and scale AI solutions with proven frameworks, senior expertise, and a partnership model that aligns to your success.
No sales pitch. Just a conversation about your AI challenges and how we can help.
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