The team behind
your company's AI.
From a 20-person clinic to a national bank, Rowth.ai brings senior practitioners who take you from AI experiments to production, and we build on top of the systems you already run. No rip-and-replace.
A model in a notebook isn't a product. We close the gap.
Most enterprise AI dies between the prototype and production: at integration, governance, or change management. We own the whole path, not just the fun part.
Going it alone Without Rowth
- Months hiring an AI team before a line of code ships
- Prototypes that impress in a demo and stall in production
- No MLOps, so models silently decay after launch
- Rip-and-replace projects that fight your existing stack
- Governance and compliance bolted on at the end
With Rowth.ai Production-first
- Senior pod embedded with your team, shipping from week one
- Every model built to deploy: behind SLAs, monitored, owned
- MLOps, drift detection and evals wired in from day one
- Layers on top of the tools you already run, no rip-and-replace
- Responsible-AI framework and audit trail built in
We implement AI on top of your current structure.
You've invested years in your data, your apps and your cloud. Rowth adds an intelligence layer that plugs into all of it, so you get production AI without re-platforming anything.
Wherever you are with AI, there's a way in.
Every size, every industry, every level of AI maturity. We meet you where you are and build from there.
Start without betting the company
A focused 60-day audit and one high-ROI use case. We prove value on top of your current tools before you commit to anything bigger.
Get out of pilot purgatory
You have promising prototypes that never shipped. We take the best one to production, with MLOps, governance and a clean handoff.
Make AI an operating capability
Multiple use cases, multiple teams. We stand up an AI platform and COE so the whole organisation can ship on shared, governed rails.
Everything you need to ship real AI
From the strategy that precedes any code to the plumbing that keeps it running, one accountable team owns the outcome.
Highest-signal context, not just similarity
Custom LLMs, retrieval with citations, and agents that take action, tuned to your domain, with deployable guardrails.
Scales like infrastructure
High throughput, low latency, continuous training, drift detection and automated rollback. The unglamorous parts done right.
Always fast
Context from every source
Business data, chat sessions, documents, unified.
No chained waiting
Everything executes at once, so the system stays fast under load.
A clean, deterministic delivery, every time
We assemble strategy, models and operations into one reliable engagement, layered onto your existing stack.
Most AI projects peak at the pilot, then decay.
- Prototypes impress in a demo, then quietly rot without MLOps
- In-house teams get pulled to other fires after launch
- Model accuracy drifts as the world changes around it
- Result: the business loses trust right when it matters most
Production outcomes, not just promises.
Across the dimensions that decide whether AI actually ships, Rowth's embedded-pod model beats both a from-scratch in-house build and a generic consultancy.
| Dimension | Rowth.ai | In-house build | Generic vendor |
|---|---|---|---|
| Time to first production model | 6 weeks | 7 months | 4 months |
| Senior practitioner ratio | 80% | 20% | 35% |
| MLOps & monitoring included | Always | Rarely | Add-on |
| Works on your existing stack | Always | Varies | Rip & replace |
| Responsible-AI framework | Built in | DIY | Optional |
| Knowledge handed to your team | Always | N/A | Limited |
Core delivery architecture
Built for the workflows that run your business
The value of AI is domain-specific. These are the four industries we have published production work in — pick one to see what changes when Rowth owns the engagement.
Articles and video matched by meaning rather than keywords, with new content embedded and linked in the background as it publishes.
Editors map clips to articles by hand, keyword matching returns the wrong video whenever the wording differs, and the archive is unsearchable by meaning.
Engagement models for every stage
Start with an honest audit. Scale to embedded pods. No lock-in, no surprise SOWs.
- 60-day readiness audit
- Use-case scoring & ROI model
- Data & governance gap analysis
- Prioritized roadmap
- One scoped use case to PoC
- Senior 2–3 person pod
- Working model + eval harness
- Go / no-go recommendation
- Embedded multi-disciplinary pod
- Production deployment + MLOps
- Weekly demos, named owners
- Layers onto your existing stack
- Multiple parallel pods
- AI COE setup & enablement
- Dedicated Slack + advisory
- Self-host / sovereignty options
Frequently asked questions
No. Rowth is deliberately additive: we build an intelligence layer on top of the data, apps and cloud you already run, and integrate with them. No rip-and-replace, no re-platforming.
Every size. We've taken 20-person teams from zero AI to a first production use case, and stood up AI platforms for national enterprises. The engagement model scales to fit you.
Yes. Every engagement is built around your data, your stack, your KPIs and your regulatory environment. We assemble the pod and the scope to fit; there is no off-the-shelf package.
No. We handle strategy, build, deployment and ongoing operations, and always enable your team with documentation and runbooks so you own what we build.
A readiness audit takes about 60 days. A scoped sprint produces a working proof of concept in roughly 8 weeks. Embedded pods typically have a first model in production by week six.
We don't disappear at go-live. Engagements include monitoring, drift detection, retraining and on-call support, with a clean handoff to your team whenever you're ready.
A senior practitioner reads every brief and responds within 48 hours, with a sharp first read, not a templated proposal.