AI Services
Three AI offerings that cover how your software is built, how it is shipped, and how your organisation remembers why. Each one is a complete engagement on its own. Together they run from requirement, to release, to recall.
Hanodale Solutions builds AI into the parts of an organisation where it removes measurable effort — not as a demonstration, but as working infrastructure inside your own estate. Our AI Services practice covers the delivery pipeline that ships your software, the lifecycle that governs how it is built, and the knowledge estate that holds every decision, ticket and document behind it.
Every engagement is phased, so there is no big-bang cutover and you see working results within the first few weeks. AI reviews, explains, drafts and suggests at every point — your team keeps the merge button, the release button and the final call.
What We Offer
Six workstreams or capabilities each, every one delivering standalone value.
Takes a requirement from intake to production through five automated phases and six controlled quality gates. AI handles analysis, code review, testing and documentation; your engineers keep approval and release authority.
Takes teams from hand-copied deployments and a single shared branch to a governed, automated delivery pipeline — open source first, with minimal new licence commitment.
Turns scattered folders, closed tickets and undocumented decisions into one system that answers in plain language, with every source attached, inside your own estate.
ForgeOne by Hanodale
Gate owners are named — business analyst, tech lead, DevOps engineer, QA lead and release manager. Nothing advances on assertion alone.
Requirements arrive incomplete and the gaps surface late, in UAT, where rework is most expensive. Manual code review and regression testing throttle release frequency and vary reviewer to reviewer. ForgeOne closes both by putting AI analysis at intake and evidence at every gate.
Fits large legacy application estates, multi-team delivery, and regulated or audited environments that need documented evidence at each gate.
Contact UsDevOps Setup, Built With AI
Open source first. GitHub, Jenkins, Flyway and Plane — working results from Week 4, with production deliberately untouched in Phase 1.
Manual deployments, one shared branch, schemas reconciled by text-compare and bugs tracked in spreadsheets all cost more than they appear to. We put in a governed pipeline and embed AI where it removes real effort — advisory at every point, never at the controls.
Fits teams with no pipeline yet, or a pipeline the engineers do not trust enough to use.
Contact UsCortexOne — Enterprise Knowledge
Answers only from your own material. Where the record does not support an answer it says so, and every claim carries its source.
Search returns files, not answers. The same questions are asked every week, knowledge leaves with the leaver, and new joiners take months to ramp. CortexOne indexes what you already have and answers on it in plain language, with the document, page and date attached.
Fits organisations where institutional knowledge walks out the door and ramp-up drags on for months.
Contact UsHow They Fit Together
Lays the pipeline the work runs on — branches, approval gates, versioned migrations, environments and rollback. Nothing above it is reliable until this exists.
Runs delivery on those rails, with AI at every phase and evidence at every gate, so defects and rework are caught long before they reach UAT.
Retains what was built and why — decisions, tickets, documents and precedent — and answers on all of it in plain language, with sources.
There is no dependency in either direction. Start wherever the cost is highest today; the sequence above is the common one, not a prerequisite chain.
Side by Side
What differs across the three, so the starting point is a decision and not a guess.
| Dimension | ForgeOne | DevOps Setup | CortexOne |
|---|---|---|---|
| Typical duration | Phased — pilot, extend, scale | 14 weeks end to end | 8 weeks to first go-live |
| Unit of work | 5 phases, 6 gates, 5 test levels | 6 workstreams | 6 capabilities |
| Core stack | Jenkins orchestration with tuned AI agents | GitHub · Jenkins · Flyway · Plane | Vector + graph store, OCR & layout pipeline |
| Where AI sits | Analysis, code review, testing, documentation, self-healing | PR review, build failure analysis, migration review, release notes, triage | Answer synthesis, document understanding, gap detection, drafting, triage |
| You keep | Approval and release authority at all six gates | The merge button and the deploy button | The decision — AI retrieves, explains and drafts only |
| Primary outcome | Rework removed before UAT, cycle time compressed | Repeatable, low-risk, documented releases | Answers in minutes, with precedent and provenance |
| Cost basis | Usage-based AI on existing CI infrastructure | ~US$70–120 / month new tooling, ten-person team | Scales with query volume rather than seats |
| Runs in | Your repositories | Your infrastructure | Your estate — data never leaves it |
How We Work
Gates mirror your real sign-off structure. AI advises at every point; it never releases and never decides.
Everything runs inside your estate where policy requires it. No organisational content trains an external model.
Minimal new licence commitment. Where a paid component earns its place, the cost basis is stated up front.
Runbooks, training and advisory are built into every phase, not bolted on at handover.
Outcomes
Ways to Engage
A two-week discovery on any one offering, producing a costed and phased plan.
Any single programme, delivered end to end by our team.
All three sequenced, with one shared discovery and one governance model.
Gates, AI touchpoints or new knowledge domains added to what you already run.
Tuning, support and quarterly review after go-live.
One module, one squad — we baseline it and show where the rework actually goes.
We baseline one of your delivery streams or knowledge domains and show where AI removes the most effort — before you commit to a full rollout.