Karya · AI Consulting
AI strategy that becomes
real implementation.
Most organisations know they need AI, but not what kind, where, why, or how. Karya helps you choose the right intelligence, design the system around it, and ship something that scales — not a prototype that stalls.
02 · The Problem
From noise to necessary.
The earliest artefacts of AI work — clever prompts, scrappy demos, sticky-note ideas, “works on my machine” — feel like progress. They aren’t. Without strategy, architecture, workflows and an implementation plan written down and wired together, the work cannot scale past the prototype it started as.
01
Unclear use cases
Teams agree AI matters but cannot say which problem to solve first, or why this one before that one.
02
AI where automation would do
Glamour wins. Deterministic tooling — which would solve the problem more cheaply and reliably — gets skipped.
03
Pilots that do not scale
The demo works. The production version dies on integration, cost, latency, or trust.
04
Dependency without ownership
A vendor or contractor builds the system. When they leave, the team cannot evolve it — so it stops.
05
Workflow reality ignored
The model is fine. The way humans actually use it inside the workflow was never designed.
03 · The Diagnosis
Recommendation begins with observation.
Before designing anything, we map the work as it actually runs. What comes in, who decides what, where the time leaks, which systems contradict each other. The pattern is consistent enough to predict — and the design then targets it precisely.
01
Time leaks
Hand-offs, intake queues, and idle stretches that no dashboard ever surfaces.
02
Context loss
Brief, email thread, doc, call summary — four sources, one decision, never aligned.
03
Repetition
The same copy / paste, manual cleanup, format rebuild — happening on every cycle.
04
Decision stalls
Reviews that sit three to seven days waiting on a single missing input.
05
Existing systems
CRM, warehouse, sheet trackers, homegrown tools — each one a design constraint, not a blank canvas.
04 · The Toolkit
Four kinds of intelligence.
Often the answer isn’t AI — it’s automation, software, or a decision system. We pick the lane by the problem in front of us, and combine them when the work actually demands it.
- Automation: For repeatable workflows with predictable rules.
- Software: For structure, ownership, databases, interfaces, permissions, and scale.
- AI: For language, context, summarisation, extraction, reasoning, and adaptation.
- Decision Systems: For ambiguity, trade-offs, evidence, scenario planning, and executive judgment.
05 · From Prototype to System
From prototype to production.
Most AI demos die in the gap between “works on my machine” and works in production. We close it with an eight-stage pipeline — discover, design, architect, build, evaluate, harden, deploy, operate — and a nine-point hardening checklist that catches what casual deployments don’t.
01
Architecture
A design that owns the model, the data, and the deployment surface end-to-end — not three vendors stitched at the seams.
02
Evaluation
Measurable quality gates per release. Vibes are a starting point, not a shipping criterion.
03
Data
Provenance, versioning, retention. Sensitive paths drawn explicitly, not assumed.
04
Security
Authn / authz, secrets management, encryption at rest and in flight — to the standard of the industry, not the prototype.
05
Fallbacks
Graceful degradation when the model is wrong, slow, or unavailable. The product still works.
06
Observability
Traces, logs, metrics per call, user, and cost centre — so failure is investigable, not mysterious.
07
Cost
Token spend tracked at the unit and the cohort. Budgets enforced. Surprise bills designed out.
08
Human review
The loops where people catch what the system can't — and the ones we remove so people don't have to.
09
Ownership
A named team inside your company that can evolve the system long after we leave.
06 · Ownership
Your business should not become dependent on a black box.
Most AI engagements end with a system you can’t open, can’t audit, and can’t evolve without the team that built it. We end with the opposite — documented architecture, named owners inside your team, an audit trail per decision, and a codebase that ships under your repository.
01
Clear architecture
Every component named, every interface documented, every decision traceable to a problem statement — not buried in a vendor's slide deck.
02
Documented decisions
A date-stamped log of what was chosen and why. The next team understands the system without having to interview the previous one.
03
Controlled integrations
Every external dependency declared, isolated and replaceable. No black-box vendor lock-in disguised as a feature.
04
You own the codebase
The repository ships under your organisation, signed and audited. Named engineers on your team can evolve the system long after we leave.
07 · What We Deliver
Strategy to production. Not prompts.
Systems.
Thirteen disciplines that span the work — from finding the right opportunity, to choosing what to build, to shipping it at scale without the production line falling apart. Built with intent. Scaled responsibly.
- AI opportunity discovery: Identify high-impact use cases aligned to business outcomes.
- Workflow and process mapping: Map existing workflows to uncover friction, gaps, and automation potential.
- Automation roadmaps: Prioritised roadmaps with clear use cases, timelines, and value.
- Build vs buy analysis: Objective analysis to determine the right build, buy, or partner approach.
- Tool and model evaluation: Evaluate tools and models for fit, performance, and total cost.
- AI architecture design: Secure, scalable architectures aligned to your technical landscape.
- Prototype-to-production planning: De-risk delivery with a clear path from prototype to production at scale.
- Internal AI systems: Build internal tools and copilots that improve team productivity.
- Knowledge bases and RAG systems: Design and build retrieval systems grounded in your enterprise data.
- Agent workflow design: Design agentic workflows that complete real work across systems and data.
- Governance and security recommendations: Practical guardrails for responsible, secure, and compliant AI use.
- Cost and latency planning: Model cost, latency, and scale to optimise performance and spend.
- Team adoption strategy: Drive adoption with training, change management, and enablement plans.
08 · Begin
The next move
is yours.
Thirty minutes. No deck. Tell us where AI is stuck — or where you suspect it might be — and we’ll tell you whether Karya is the right team to help. If not, we’ll point you to who is.