Solutions | AI Productivity
AI that makes your own team the most productive in the market
Agentic engineering for your developers and Kiro-powered tools for your ops, growth and exec teams. One playbook for every internal function — built on AWS.
Book a productivity discovery call
AWS Advanced Tier
Internal AI tools
Measured ROIWhy internal AI tools fail to create real productivity
Everyone's adopting AI for their customers. Almost no one is adopting it for themselves — and the internal leverage the board approved spend for never materialises. The ROI stays theoretical. The adoption stays patchy.
Dev teams: tools without method
Copilots scattered across the team. No agreed playbook, no shared prompts, no SDLC redesign — everyone's using AI differently.
FIX
We redesign the build loop, not just add another coding tool.
Ops & growth: AI without workflow
AI gets dropped on top of existing processes instead of replacing them. The tool nobody opens. The prompt library nobody maintains.
FIX
We build tools around named workflows and retire the old way by a specific date.
Exec: dashboards without decisions
AI-generated summaries land every Monday. Nobody acts because the insight isn't actionable and there's no decision loop built in.
FIX
We design around the decision, not the report.
A practical path from scattered AI usage to measurable productivity gains
Start with one workstream or run both in parallel, depending on where productivity gains are easiest to prove.
Track A
Agentic Engineering for developers
Redesign the SDLC around agentic build loops, shared context and measurable engineering output.
Includes:
Output
Productivity baseline + engineering adoption plan
Track B
Kiro Internal Tools for ops, growth & exec
Build internal AI tools that replace spreadsheets, reporting, triage and repetitive operational workflows.
Includes:
Output
Working tools + named users + old workflow retired
SHARED LAYER
Adoption, measurement & next wave
Every tool ships with named users, adoption targets and ROI dashboards — so productivity is measured, not assumed.
Includes:
Output
ROI dashboards + adoption targets + second-wave roadmap
What you get from the programme
Real adoption: Named users, real workflows and the old way retired by a specific date.
Measured payback: ROI dashboards showing time saved, output volume, adoption and cost-per-run.
A capability, not a vendor: Build-operate-transfer by default, so your team owns the tools after handover.
A second wave ready: Once the first tools work, the next workflows are already identified and scoped.
Productivity only counts when the workflow changes
We don't measure productivity by whether someone opened an AI tool. We start with the workflow, baseline the current cost, build the replacement, assign named users and measure whether the old way actually disappears.
Replace the workflow, not just the task.
Every productivity build starts with the same question: what manual process should stop when this tool launches?
WHAT WE PRIORITISE
Named workflow
What manual process it replaces
Named users
Who uses it every week
Baseline metric
Time, volume or cost before AI
Adoption target
What usage needs to look like
Productivity gains without losing auditability.
In financial services, internal AI tools need to speed up analysis, reporting and decision support without creating opaque workflows. We prioritise use cases where time savings, adoption and auditability can be measured from day one.
WHAT WE PRIORITISE
- Manual reporting replaced
- Analyst time saved
- Audit-ready workflow
- Named team adoption
- Cost-per-run tracked
Turn internal bottlenecks into measurable operating leverage.
B2B SaaS teams often lose time in reporting, customer analysis, support triage, product feedback and growth operations. We build AI tools around the workflows that slow teams down — then measure adoption, speed and ROI after launch.
WHAT WE PRIORITISE
- Workflow bottleneck
- Feature delivery speed
- Support / ops time saved
- Retention workflow leverage
- Adoption and ROI tracking
Replace the workflow, not just the task.
Every productivity build starts with the same question: what manual process should stop when this tool launches?
WHAT WE PRIORITISE
Named workflow
What manual process it replaces
Named users
Who uses it every week
Baseline metric
Time, volume or cost before AI
Adoption target
What usage needs to look like
Choose the right productivity engagement
Funded Productivity Discovery
Two-week discovery on your top three workflows. AWS-funded. You come out with a costed build proposal per workflow.
- Top three workflow identification and baseline
- Tool design per workflow
- Build proposal with AWS funding confirmed
Cost
AWS-funded
Duration
2 weeks
Phased Productivity Programme
Full Discover → Build → Adopt programme for a portfolio of internal tools. Fixed-fee per phase. Co-funded by AWS where eligible.
- Full workflow inventory across all four audiences
- Tool build per phase — one audience at a time
- Adoption plan with named rollout and handover dates
- Quarterly ROI review cadence
Cost
Fixed-fee phases
Duration
4 months
Build Day / Sprint
You know exactly which tool you want. We build it. 1–6 engineers, build day or distributed sprint.
- Spec agreed before build begins
- Named user and retired predecessor confirmed
- ROI dashboard included
Cost
AWS-funded
Duration
1–4 weeks
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Ready to turn internal AI into measurable productivity?
Book a focused 20-minute conversation. We'll help you identify the workflows worth replacing, what to baseline first, and whether AWS-funded discovery could apply.
Why talk to us:
Workflow-first AI recommendations
Named users and adoption targets
ROI dashboards from day one
Build-operate-transfer, not vendor dependency
Start with a focused 20-minute conversation about your goals — no pressure, no commitment.