Licensing & renewals
Procurement, tier selection and renewal timing. We model Cloud cost against what you actually use before you commit to a number.
Atlassian value-added reseller · AI-first solution provider
Where Atlassian meets AI-first transformation.
We license and implement the Atlassian stack — Jira, Confluence, Jira Service Management and the apps around them — and we build the AI that makes it faster: agentic workflows, custom agents, tuned models, and inference that costs a fraction of what it did.
Start the transformation Book a working session
- Atlassian licensing & migration
- Agentic workflows in Jira
- Custom agents & evals
- Fine-tuning & quantization
- AI-assisted product management
Scroll to travel between worlds — or click a node in the scene.
01 — Atlassian
The Atlassian stack: sold, built, migrated, and run.
As a value-added reseller we handle the commercial side — licensing, renewals and optimisation across Jira, Confluence, Jira Service Management and the Marketplace. As engineers we handle everything after the purchase order.
Server / Data Center → Cloud
Migration planning, app compatibility review, test runs, and a cutover that people notice mostly because it worked.
Implementation & administration
Project and workflow design, permission schemes, automation rules, service desks, and the admin hygiene that stops a healthy instance becoming a haunted one.
Forge & Marketplace apps
Custom Forge apps when the Marketplace has no answer — plus integrations that keep Jira and Confluence in step with everything else you run.
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02 — Agentic workflows
Multi-step workflows where the agents do the walking.
We design workflows that run inside the tools your teams already open every morning. Agents pick up work, gather context, draft, and hand off — with a human gate wherever the consequence is real.
- 1Ticket createdJira issue lands with intake data attached.
- 2Agent triagesClassifies, dedupes, finds related issues, sets priority.
- 3Agent draftsPulls context from Confluence, proposes a fix or a reply.
- 4Human approvesA person signs off; the workflow records who and why.
- Human-in-the-loop gates. Approval steps are explicit, not implied. Nothing irreversible runs unattended.
- Full audit trail. Every agent action is logged with its inputs, so “why did it do that?” has an answer.
- Evaluated, not vibed. Each workflow ships with a test set and a regression run before it goes near production.
- Bounded cost. Token and tool budgets per workflow, with alerts before the invoice teaches you.
03 — Agent development
Custom agents with real tools and real evaluations.
An agent is only as good as the tools it can reach and the tests that hold it honest. We build both, then leave you the harness so you can add the next one yourself.
Tool use & integrations
Typed tools over your internal APIs, Jira and Confluence, files and databases — with permissions matching the person the agent acts for.
Memory & retrieval
Grounded retrieval over your own knowledge, plus the plumbing that keeps it fresh when the source changes.
Evals & guardrails
Task suites, failure taxonomies and automated scoring. We tell you the failure mode before we tell you the demo.
04 — Model building
Models tuned on your data, not on someone’s demo.
When a prompt can’t get you there, we build the model: dataset curation, fine-tuning, evaluation, and the serving path that puts it in front of users.
Data & ML engineering
Pipelines, splits that don’t leak, labelling workflows, and lineage you can defend in a review.
Fine-tuning
Task-specific tuning where it beats prompting — and an honest answer when it doesn’t.
Evaluation
Held-out sets, regression suites, and the metric we agreed on before anyone wrote code.
05 — Quantization & optimization
Smaller, faster, cheaper — and you get to feel it.
Drag the slider and watch the geometry drop from full precision to 4-bit. Same idea as the real work: INT8, INT4, distillation, and serving tuned on cost per token and p95 latency.
- Relative size
- 1.00×
- Relative speed
- 1.00×
- Quality retention
- baseline
Illustrative figures for the interaction only — not a benchmark of your workload. We measure before we quote.
06 — Product management
AI-assisted product strategy, discovery and delivery.
The delivery engine is only useful if you’re building the right thing. We work the product side too: discovery, roadmap, prioritisation, and the metrics that tell you whether any of it worked.
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Weeks 0–1
Discovery
Interviews, workflow mapping, and a written point of view on where AI actually pays.
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Weeks 1–2
Strategy
Opportunity sizing, build-versus-buy, and the licence spend that funds it.
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Weeks 2–3
Roadmap
Sequenced bets with owners, a definition of done, and a measurable target each.
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Week 3 onward
Delivery
Two-week increments, demos in your instance, and a handover you can run.
07 — Founders
Built by operators who have shipped.
We have sat in your seat: finance technology leadership inside a top-tier bank, a bedside-to-boardroom nursing career, a startup of our own, and the classroom.
Jabreal Johnson
Co-founder — technology, product & delivery
An attorney who studied software engineering, Jabreal has founded a startup, taught at the university level, and spent years as a technology leader in finance at a top-tier bank. That mix is the point: legal rigour, engineering depth, and the discipline of running a product that has to survive an audit, a board, and a release calendar.
Marwa Slim’Johnson
Founder — Transformation Titans
A registered nurse in Tunisia and the United States, Marwa founded Transformation Titans as a technology consulting partner that puts Atlassian and AI first. She brings the clinical view of how a workflow actually runs — where the handoffs break, what a person needs on screen at the moment of decision — to the design of the systems we deliver.
08 — Start
Tell us the workflow that hurts.
We’ll reply with a point of view, a rough shape for the work, and what we’d need to see to be confident. Usually within one business day.