About Olive Software
Olive Software is a software development company that turns ideas into working products. Olive Software focuses on AI & Machine Learning. With 2 - 9 people on staff, Olive Software can take on projects of varying size. Typical hourly rates run around < $25 / hr/hr. Olive Software holds a 5.0-star rating across 14 client reviews. This gives a quick snapshot of what to expect when working with Olive Software.
Where We Specialize
Industry Expertise
Typical Project Size
Olive Software Reviews
Write a ReviewRoadmap that finally put our infrastructure investment in language the CFO could approve
Niamh O'Sullivan / Director of Product - Munster Digital LtdJun 07, 2026
Project summary: Our audience data was fragmented across eight tools with no single identity layer. Personalisation had become impossible without first solving the data foundation.
The integration layer was the part of the project I was most concerned about going in. Our system landscape is complex, several of the upstream APIs we relied on were poorly documented, and two third-party vendors had a history of unpredictable response times on integration questions. This team managed all of that. They documented what the upstream vendors could not, built resilience into the integration architecture where the upstream behaviour was unreliable, and delivered a solution that works as specified in production. I could not have asked for more.
Clear and consistent communication adapted appropriately for both technical and non-technical stakeholders, shared tooling that gave our team real-time visibility, reliable sprint delivery throughout
Their discovery process is more rigorous than we were accustomed to and required more preparation from our side than we had initially allocated — but the quality of what followed justified every hour of it
Questions & Answers
Software that solved the actual problem rather than the stated one — a crucial difference
Kelsey Drummond / Director of Digital Health - Crestline Health PartnersJun 02, 2026
Project summary: Our internal product thinking was strong but our execution capability in this specific technology domain was limited. We needed depth, not generalism.
The project brief was ambitious and we had received proposals ranging from two to five times our eventual budget from other vendors. This team came back with a proposal that was commercially realistic and technically credible — and then delivered against it. That alignment between proposal and outcome is not something I take for granted. I have been on the other side of it enough times to know it requires both honesty in the sales process and discipline in delivery. We experienced both.
Production system that has performed as specified since go-live without remediation work, documentation thorough enough to support internal maintenance, knowledge transfer that left our team genuinely capable
Pipeline availability for kickoff required a few weeks of lead time — in hindsight that selection pressure means you are working with a team that is in demand for the right reasons
Questions & Answers
Test automation that our developers adopted as a genuine aid rather than a compliance exercise
Zara Hussain / Head of Technology - Ravi Digital AgencyJun 01, 2026
Project summary: Lean manufacturing initiatives required real-time OEE data at the line level. Our existing systems could not provide it without significant manual aggregation.
Six months after go-live our platform is processing three times the transaction volume we specified in the original brief. The architecture choices made during discovery accommodated that growth without remediation work. That is the difference between a team that designs for what you tell them and a team that designs for what you are likely to need. We are in conversation about a Phase 2 engagement and I expect to be using this partnership for several years.
Deep domain knowledge that reduced the discovery overhead significantly, proactive risk identification before issues became incidents, delivery cadence that our stakeholders found reassuring
We underestimated the input required from our subject matter experts during the requirements phase. The team flagged this early but our resource planning did not fully reflect it — our responsibility, not theirs
Questions & Answers
Mixed reality build that landed exactly where the brief pointed and then went further
Hyun-Su Lim / Director of Platform - Hanam Tech SolutionsMar 28, 2026
Project summary: Several years of incremental development had left us with a platform that was technically functional but strategically limiting. A structured rebuild was the agreed path forward.
The project brief was ambitious and we had received proposals ranging from two to five times our eventual budget from other vendors. This team came back with a proposal that was commercially realistic and technically credible — and then delivered against it. That alignment between proposal and outcome is not something I take for granted. I have been on the other side of it enough times to know it requires both honesty in the sales process and discipline in delivery. We experienced both.
Delivery timeline that proved achievable rather than optimistic, estimation accuracy that reflected real analysis rather than competitive bidding, scope discipline that prevented the feature creep we had experienced before
Pipeline availability for kickoff required a few weeks of lead time — in hindsight that selection pressure means you are working with a team that is in demand for the right reasons
Questions & Answers
Mobile experience so polished that users have been sending us compliments
Aarav Mehta / Chief Data Officer - Zenith FinServ LtdMar 12, 2026
Project summary: Rapid growth had created a skills gap on the platform engineering side of our business. We needed an experienced partner to close that gap while our internal team scaled, without compromising quality or timeline.
We gave this team an aggressive timeline, a technically complex scope, and a client-side project team that was stretched thin and not always available at the speed the engagement required. They absorbed all of that gracefully. Where they needed input they were precise about what they needed and when. Where they could proceed independently they did. The result was a delivery that landed on time despite the constraints on our side, which I regard as evidence of genuine professional maturity.
Deep domain knowledge that reduced the discovery overhead significantly, proactive risk identification before issues became incidents, delivery cadence that our stakeholders found reassuring
Time zone coordination required some deliberate overlap management from both sides in the first couple of sprints, after which we had an efficient async rhythm that worked for the whole project
Questions & Answers
Predictive models that have measurably changed how we approach every planning cycle
Tobias Lindemann / Leiter Digitalisierung - Lindemann Industrie GmbHFeb 20, 2026
Project summary: Warehouse management inefficiencies were adding cost and introducing errors at a rate that was becoming visible to clients. A modernised WMS was the agreed solution internally — we needed a partner to build it.
Six months after go-live our platform is processing three times the transaction volume we specified in the original brief. The architecture choices made during discovery accommodated that growth without remediation work. That is the difference between a team that designs for what you tell them and a team that designs for what you are likely to need. We are in conversation about a Phase 2 engagement and I expect to be using this partnership for several years.
Delivery timeline that proved achievable rather than optimistic, estimation accuracy that reflected real analysis rather than competitive bidding, scope discipline that prevented the feature creep we had experienced before
Their discovery process is more rigorous than we were accustomed to and required more preparation from our side than we had initially allocated — but the quality of what followed justified every hour of it