AI that ships.Earns its keep.
Most AI pilots stall in a notebook. We take the ones worth scaling and put them into production, governed, measured, and adopted, so the value shows up in your numbers, not just your roadmap.
- 20 to 40% faster case handling
- A typical cycle-time improvement on support and back-office workflows once AI assist and automation are in production.
- Governed by default
- Role-based access, audit logging, and model oversight are part of every build, not an afterthought.
- Built to be used
- Enablement plans, playbooks, and usage analytics help teams adopt the new way of working and keep it.
The problem
Your AI pilots impress, but never ship
Teams buy tools and run impressive demos, then stall before anything reaches a real workflow. Pilots drag on, adoption never lands, and leadership quietly writes off the budget. The technology was rarely the problem. The path from demo to production was.
Common symptoms
Prototypes live in notebooks and demos. Nobody on the front line ever touches them.
No agreed way to judge accuracy, cost, or business impact, so no one can say if it worked.
Security and compliance questions surface at the end and quietly block go-live.
What this costs you
Pilot budgets spent with nothing in production to show for them.
A widening gap between the AI you talk about and the work you actually run.
Teams fall back to manual habits while competitors quietly automate the same work.
The Assist, Augment, Automate framework
From experiment to scaled delivery
Not every task deserves the same level of autonomy. We classify each use case by risk tier, so a low-stakes draft moves fast while a regulated decision gets human approval gates, red-team testing, and audit-ready documentation before it ever runs on its own.
Low risk (assist)
Summarising, drafting, and search. The AI proposes, a person decides, and nothing acts without a review.
You get
Prompt templates, an evaluation suite, and a usage telemetry dashboard.
Medium risk (augment)
Recommendations, triage, and classification. The AI shapes the decision, but a person signs off before it counts.
You get
Approval workflow, accuracy benchmarks, fallback rules, and a clear escalation path.
High risk (automate)
End-to-end automation of regulated or high-value workflows. The AI executes, and every action is logged and reversible.
You get
Red-team report, compliance mapping, rollback controls, and a model governance card.
What we deliver
Built to earn adoption and trust
No black boxes and no vendor lock-in. Every capability below is built to be safe, measurable, and actually used by the people who do the work.
Discover and Secure
Discover
Use-case discovery and ROI
We find the workflows where the friction and the money actually are, then put a number on the win before you commit.
Secure
Guardrails
Privacy, access control, and red-teaming built in from the first line, not bolted on before launch.
Measure and Embed
Measure
Evaluation and analytics
Clear measures for accuracy, adoption, and value, so you can prove what worked instead of guessing.
Embed
Change and adoption
We embed AI into the tools and habits people already have, so it gets used instead of avoided.
Connect and Sustain
Connect
Architecture and integration
Secure, clean connections into your apps, data, and workflows, with no shadow IT and no surprises.
Sustain
Model lifecycle and monitoring
Deployment, monitoring, retraining triggers, and guardrail operations your own team can run.
Is this right for you?
This is a fit when the work already exists
If three or more of these describe your week, a discovery call is worth 30 minutes of it.
Your team handles 50 or more similar documents or cases every week.
OperationsMonth-end close or reporting still depends on manual data pulls.
FinanceSupport agents spend most of their day on routine, repetitive questions.
Customer SupportContract review or policy approval is a reliable bottleneck.
Legal & ComplianceYou have tried AI tools but cannot point to a single measurable result.
All teamsSecurity or governance concerns have killed an AI rollout before.
IT & InfoSecYou are scaling a process across teams and the quality varies too much.
OperationsHR or L&D answers the same handful of questions over and over.
HRIncident triage or alert routing is still mostly done by hand.
IT & InfoSecLeadership wants proof of AI ROI before signing off on more investment.
FinanceHow it works
Your maturity roadmap
Every engagement runs the same clear path, from diagnosis to delivery to a clean handover.
Foundation
Prioritise use cases
We weigh pain, feasibility, and ROI together, so the first release lands on a workflow that actually matters.
A scored use-case backlog with risk tiers and a business case.
Validation
Design safe architecture
We set the data boundaries, prompts, integrations, and approval controls before a single thing ships.
Architecture blueprint, security model, and integration spec.
Production
Pilot and prove it
We run a contained pilot against real baselines, with QA tests and adoption tracking from day one.
An evaluation report with accuracy, adoption, and ROI numbers.
Scale
Scale and hand over
We standardise the pattern, train your owners, and operationalise monitoring so the value sticks without us.
Runbooks, retraining triggers, and a process-owner enablement pack.
Proof, not promises
20 to 40% faster case handling
A typical cycle-time improvement on support and back-office workflows once AI assist and automation are in production.
Governed by default
Role-based access, audit logging, and model oversight are part of every build, not an afterthought.
Built to be used
Enablement plans, playbooks, and usage analytics help teams adopt the new way of working and keep it.
From practice
How we think about what works
The teams that move fastest are not the ones with the biggest AI budget. They pick one workflow, prove the numbers, and scale the pattern that worked. Start narrow, prove it, then go wide.
AI & Automation Practice Lead, SolvX
Case story
A common starting point: a company comes to us after a stalled internal pilot, with months of work and nothing in production. We scope a single high-friction workflow, the kind with clear volume and clear cost, and prove it end to end before anyone talks about scaling.
Results
- A measurable drop in cycle time on the target workflow within the pilot window
- Pilot evidence the leadership team can take into a budget decision
- An internal team trained to own and improve the solution without outside help
Engagement paths
Choose the right starting point
Every engagement is scoped to your situation. Pick the path that matches where you are today.
Assessment
2 to 4 weeks
Best for: Teams exploring AI opportunities but unsure where to start.
- Scored use-case backlog
- Risk-tier classification
- Business case summary
- Recommended pilot scope
We need: Business sponsor, access to 2 to 3 target workflows, and subject-matter experts for validation.
Pilot
6 to 10 weeks
Best for: Teams ready to prove value on one high-impact use case.
- Working AI solution in your environment
- Evaluation report with ROI metrics
- Security and governance documentation
- Adoption playbook
We need: Defined use case, data access, and a process owner to validate outcomes.
Build & Scale
3 to 6 months
Best for: Teams scaling proven pilots across functions or business units.
- Reusable AI patterns and prompt libraries
- Monitoring and retraining automation
- Centre of Excellence setup
- Cross-function rollout plan
We need: Executive sponsor, platform team alignment, and governance framework.
Honest answers
The questions that actually get asked
No polished softballs. These are the concerns leaders actually raise before they sign anything.
If your people lose real hours to repetitive, document-heavy, or decision-support work, automation will give time back. We start with a discovery session to find the use cases worth doing and, just as honestly, the ones that are not.
Most pilots run 6 to 10 weeks: baseline measurement, design, build, evaluation, and a stakeholder review. We track cycle-time, quality, and adoption from week one, so you are never guessing at the end.
We scope by use case. A focused pilot is a fixed-scope investment covering discovery, build, and evaluation, so you know the number before you commit. We share a detailed estimate right after the discovery session.
A business sponsor, access to the target workflow and its data, and 2 to 3 subject-matter experts who can validate outputs during the pilot. That is genuinely enough to begin.
Your data stays in your tenant. We enforce least-privilege access, build in approval workflows and logging, and align guardrails to your governance requirements before anything goes live. Security is part of the build, not a final-stage hurdle.
Usually both. We map each use case to the right pattern: off-the-shelf where speed matters most, custom where differentiation or data sensitivity makes it worth the effort. We will tell you when custom is not worth it.
You do. We hand over model monitoring, feedback loops, retraining triggers, and enablement for your process owners, so the value lasts. All code, models, and infrastructure stay in your environment. You are never locked in.
We set baseline metrics in week one (cycle time, error rate, volume handled) and compare them against post-deployment performance. We share the numbers with sponsors at pilot close and again at quarterly reviews, good or bad.
Next step
Where do you want to start?
Pick the path that matches where you are today. Both start with a straight conversation.
Map your next move with AI & Automation
Book a 30-minute discovery call. We review your workflows, point out the quick wins, and outline a practical pilot plan you can act on.
- We review your highest-friction workflows
- We pick out 2 to 3 pilot-ready use cases
- You leave with scope, timeline, and expected outcomes
No obligation. Typical response within 1 business day.
Start with an assessment
Not ready for a full pilot yet? A 2 to 4 week assessment gives you a scored use-case backlog, a risk-tier classification, and a business case, with no obligation to go further.
- Pinpoint your 2 to 3 highest-value AI use cases
- See the risk and governance implications up front
- Get a recommended pilot scope with an ROI estimate
No obligation. Typical response within 1 business day.
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