Strategic Systems International and the business of keeping what works

A financial reporting system can grow old without becoming unimportant. Its screens date. Its programming language becomes a recruitment problem. Somewhere inside it, however, are the calculations that customers have trusted for years. Replacing the software means deciding which parts are merely old and which parts constitute the business.

Strategic Systems International works in that uncomfortable territory. SSI builds custom software, data platforms and applied AI systems for enterprises and technology companies. Its more interesting specialty is helping organizations change systems whose existing behavior still matters. A fresh interface is pleasant. A portfolio report that suddenly produces a different answer is rather less so.

The backlog that changed the business

Shoaib Abbasi founded SSI in 1991 to solve business problems with mathematics. The early assignments involved planning, forecasting and optimization. In the early 2000s, Shahab Ashraf helped establish an offshore development center. Then customers began asking a different question: could SSI help develop their products?

Abbasi describes an investment modeling and data client with a development backlog. SSI assembled a dedicated team. In his account, the client tripled in size over three years and was sold to Thomson Reuters for about $130 million. That was a client’s sale, and Abbasi’s recollection, rather than SSI’s revenue or an SSI funding round.

The experience encouraged a move toward full-service product development. Customers had effectively pointed out a neighboring business. The company that could help calculate a better plan could also help build the software that delivered it.

Give the old calculations a better room

One illustration is SSI’s published MarketQA case study for Thomson Reuters. The financial research application had an outdated interface and technology that made it sluggish and difficult to use. SSI says the client lacked the internal resources and expertise for the overhaul it needed.

The engineering task was to preserve the application’s useful financial functionality while developing a .NET interface. SSI reports completing the transformation within a year. Drag-and-drop interactions and a clearer query language improved how users worked with the system. This is an older project described in a case study published in 2021, rather than evidence of a current Thomson Reuters product roadmap.

The distinction matters. Enterprise modernization often involves a choice about where to intervene. Rebuild everything, and you may discard knowledge accumulated through actual use. Change too little, and you preserve the friction along with the functionality. SSI’s case presents a middle course: carry the valuable behavior forward, then improve access to it.

Benzinga supplies another example. In a separate financial media engagement, SSI worked with the onshore team on its Newsdesk content management system, data quality, financial calendars and interface. The case describes better data delivery and operational efficiency, without a numerical benchmark. Here the object being preserved was the usefulness of information arriving quickly enough for traders to act.

AI meets the code nobody wants to touch

A case study published in August 2026 describes a more recent migration: over 200,000 lines of undocumented investment code, moving from Visual FoxPro to C#. SSI says earlier modernization efforts had stalled amid tangled logic and scarce legacy expertise.

The team first manually translated part of the code to establish a verified pattern. It then used Claude and its human-AI teams, called Smart Pods, to help convert and inspect the rest. Engineers compared the modernized system against the original and investigated discrepancies.

SSI reports completing the work in six months, against its estimate of five years or more for a manual rewrite. Those figures describe a supplier-reported project and a hypothetical alternative. They are useful as a case, with considerably less authority as a universal promise.

The transferable idea is the sequence. Begin with an example whose correctness people can establish. Keep a reference system. Treat disagreement as something to investigate. This approach depends on having engineers who understand the domain and usable ways to check outputs. If neither exists, automated translation can produce a cleaner-looking mystery. That is an editorial inference from the workflow, and a good question for any buyer considering it.

A hospital has a different sort of legacy

In healthcare, the difficult inheritance includes staffing arrangements and operational constraints. SSI’s Optimé workforce platform applies forecasting, machine learning and mathematical optimization to matching staff supply with patient demand. It connects with systems including Epic, Kronos and Lawson, rather than requiring the hospital to begin with an empty desk.

In May 2026, SSI announced a partnership to make Optimé available through Cross Country Healthcare’s Intellify platform. Cross Country’s own announcement specifies an exclusive 36-month agreement. That gives SSI’s workforce technology a route into an established healthcare workforce business.

The platform includes analytics, strategy, scheduling, staffing, productivity and credential capabilities. Its practical audience includes leaders managing nursing, allied health and physician workforces. The operational question is concrete: how should available people be deployed as demand changes?

A forecast is useful only if someone can act on it. The attraction of connecting forecasting with scheduling and workforce data is that those decisions can happen within the same operational context. Buyers still need trustworthy inputs, workable constraints and staff who can make sense of recommendations. Mathematical elegance has never covered an absent shift by itself.

You are buying a working relationship

SSI’s commercial menu distinguishes between dedicated engineering teams, hybrid teams combining locations, and project teams. A dedicated team can join a client’s processes or own planning and delivery. A fixed-fee project requires a defined, detailed scope. Those arrangements put different demands on the buyer: continuing product work needs a continuing conversation; a bounded assignment needs boundaries both sides understand.

The cost question therefore starts with the shape of the engagement. Who owns requirements? How much integration is involved? Who maintains the result? A headline fee divorced from those responsibilities would tell a buyer remarkably little. For an uncertain legacy project, the discovery and verification work belong in the budget discussion.

SSI competes in a crowded market for outsourced software and product engineering. Buyers can consider specialists, larger firms such as EPAM or Globant, or expanding their internal team. SSI’s documented pitch combines analytical experience, industry familiarity and an international delivery model. None of those ingredients is exclusive. Their usefulness depends on the team assigned to a particular problem.

“I wasn’t after size, and we’re still not.”

Shoaib Abbasi, founder’s essay, October 2023

Those two reported figures suggest why relationships deserve attention. SSI attributed growth partly to larger contracts with existing clients and improved retention. Its careers material emphasizes ownership, technical depth and training. For buyers, the test is whether the people who learn the system remain available when the next difficult change arrives.

The new partners, and the old question

SSI has been extending this engineering role toward venture creation. Its 2026 partnerships with C10 Labs and 1845 Venture Studio describe technical teams working alongside early-stage businesses. The 1845 announcement places SSI in engineering and product development; the C10 collaboration also describes selective co-investment through SSI Ventures.

On September 22, 2026, SSI announced OpenAI Select Partner status. Earlier, in February 2025, it announced an upgrade to ISO/IEC 27001:2022 certification. These are relevant signals for enterprise procurement, while the proposed architecture, controls and delivery team still require their own examination.

Across these newer arrangements, the original question remains surprisingly durable: what does the customer already know, and how do you make that knowledge useful at a larger scale? SSI’s answer began with mathematics. It now includes cloud infrastructure, software engineers and AI agents. The reader can copy a modest habit from all of it: inspect what works before commissioning its replacement.