AI Consulting & Implementation
Stop experimenting. Start transforming.
Most businesses aren't short on AI ideas. They're short on the clarity to know which ones actually move the needle. We discover before we deploy — so every dollar of engineering effort lands on something that matters.
90
Days from discovery to a validated pilot
3–4
Weeks to a decision-ready roadmap
100%
Clear ROI
24/7
Monitoring & support
Organisations aren't failing because they lack ambition or tools. They're failing because activity has replaced outcomes. Pilots run in parallel with no shared definition of success. Engineering effort piles up on low-value workflows. And the business runs exactly the same as it did two years ago.
Pattern 01
Pilot purgatory
Proofs of concept are launched without a business case or a defined path to scale. Pilots live indefinitely without ever becoming production systems.
Pattern 02
Misaligned investment
High-cost engineering gets applied to low-impact problems. The highest-friction workflows — the ones that actually move margin — stay untouched.
Pattern 03
Automation before redesign
Teams optimise processes that should be fundamentally rethought. Automating a broken workflow doesn't fix it — it just makes it faster to fail.
A five-phase structured approach — FOCUS — that takes you from operational diagnosis to a funded, pilot-ready build plan, then scales what works. Engineering begins only after this clarity exists.
Find
Map where time, margin, or quality is leaking
Most AI projects fail not because of the technology, but because of a lack of alignment on strategy, processes, and outcomes.
In the Find phase, we:
- Map every workflow, decision point, and handoff
- Surface where value is silently leaking
- Align stakeholders and executive sponsors
Deliverable
Current-state workflow map with value leakage points identified
Organise
Assess what is realistically buildable
Without a shared view of data readiness and constraints, every use case looks equally urgent — and none get funded.
In the Organise phase, we:
- Assess data maturity and system readiness
- Define governance, ownership, and success metrics
- Structure a phased roadmap with clear milestones
Deliverable
Feasibility assessment across all shortlisted use cases
Commit
Commit to what moves the needle first
Fixed-scope clarity removes the ambiguity that turns discovery into endless billing.
In the Commit phase, we:
- Rank use cases by value, effort, and speed to ROI
- Remove low-impact noise from the stack
- Lock fixed-scope, fixed-price engagement terms
Deliverable
Prioritised use case stack with impact-versus-effort ranking
Unlock
Turn the case into a pilot-ready plan
Leadership doesn't need more ideas — they need a scoped investment case they can approve.
In the Unlock phase, we:
- Scope the leading use case into a pilot-ready brief
- Define expected outcomes and resource requirements
- Build a clear investment case for leadership
Deliverable
Pilot scope, investment case, and 90-day execution roadmap
Scale
Replicate what works without starting over
The real ROI comes when a proven pilot becomes a repeatable operating model.
In the Scale phase, we:
- Roll out validated pilots across teams or portfolio companies
- Embed a repeatable deployment playbook
- Govern performance against agreed P&L metrics
Deliverable
Scaling playbook with replication criteria, governance model, and phased rollout plan
The Discovery Sprint is not a workshop. It is a decision layer. At the end of the engagement, leadership has everything needed to act — without further evaluation.
A prioritised AI roadmap
Use cases ranked by business value and implementation feasibility, with a clear rationale for what gets built first and why.
Quantified business case
Each shortlisted use case tied to a specific metric — cost reduction, time recovered, margin improvement — that leadership can defend.
Pilot-ready scope
The top-priority use case fully specified: what gets built, what success looks like, who owns it, and what it costs to validate.
A clear view of what not to build
Equally important as the roadmap — knowing which ideas to shelve saves months of misdirected engineering effort and budget.
Three engagements. Three sectors. Each one beginning with a Discovery Sprint and ending with a decision-ready roadmap tied to real business metrics.
Reclaiming analyst capacity at a private investment firm
Manual deal screening and MIS tracking were consuming analyst time that should have been spent on higher-value work. Discovery identified two friction-heavy workflows where AI could intervene immediately.
Deal summaries generated at email receipt. MIS reminders fully automated. Phase 2 confirmed within 6 weeks.
Compressing a 6–8 week deal approval cycle
A developer with 30+ disconnected systems had a deal approval process spanning 20+ manual workflow stages. Discovery revealed this was an integration problem, not a technology problem.
Invoice processing pilot validated automation logic before full-scale rollout was committed. Fundable roadmap delivered.
Doubling lead conversion for an operations-led business
A home services operator was losing leads in the response window between enquiry and contact. AI-powered instant response and automated scheduling changed the conversion dynamic entirely.
Lead conversion: 14% → 29%. Estimated $500K+ in annual revenue recovered.
We partner with three types of leaders who need AI to drive measurable operational impact — not shiny trends or experiments.
SMB owner-operators
You run a strong business but inefficiency is quietly dragging on margins. You want AI that recovers time, improves conversions, and grows EBITDA — not a six-month implementation project.
Private equity sponsors
You need AI to compound value across a portfolio. You want a repeatable operating model — one that deploys successfully in one business and scales across others without starting from scratch each time.
Enterprise product teams
You have AI initiatives underway but no shared prioritisation. Pilots are running in parallel, none are clearly tied to business outcomes, and leadership wants a structured decision before the next funding cycle.
We sit between strategy-only consultancies and build-only engineering shops. We discover first. We build only when the case is made.
The typical approach
- Buy a tool, then figure out what problem it solves
- Overlay AI on a broken process and call it transformation
- Pilots don't get adopted; teams default back to spreadsheets
- No measurable ROI six months after launch
- Leadership becomes sceptical; next budget cycle is harder
Our operating model
- Start with the business: where does time, money, or margin leak?
- Prove value narrowly before scaling — scalpel, not sledgehammer
- Every system anchored to a number: hours recovered, EBITDA lifted
- Fixed-scope, fixed-fee — no open billing, no ambiguity
- Playbook designed to replicate across teams and portfolios
Common questions about working with Zeksta on AI initiatives.
What happens during the first call?
A 30-minute session to assess fit and map your highest-impact AI opportunities. We listen to your current challenges, ask targeted questions about workflows and goals, and outline whether a Discovery Sprint makes sense. No sales deck. No pre-built agenda.
What's your typical project timeline?
The Discovery Sprint runs three to four weeks and delivers a decision-ready roadmap. From there, a validated pilot typically lands within 90 days. Engineering only begins after leadership has clarity on scope, outcomes, and investment.
How do you measure ROI?
Every engagement is tied to measurable business outcomes — cost reduction, time recovered, margin improvement, or revenue impact. We define baselines and targets upfront so leadership can defend the investment and track progress against agreed P&L metrics.
Can you work with our existing systems?
Yes. During the Organise phase we assess your data maturity, system readiness, and integration constraints. We build on your existing infrastructure where possible — so you're never locked in and solutions fit how your business already operates.