Built on curiosity,
grounded in
real-world use.
Infera was founded to bridge the gap between what AI can do and what organisations actually need from it — starting with the work, not the technology.
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Where Infera came from
Infera was founded in Singapore in 2019 by a small group of data scientists and software engineers who kept encountering the same problem: organisations were curious about AI but struggled to translate that curiosity into something their teams could actually use. Pilots rarely moved to production. Results were hard to measure. Systems didn't connect with existing tools.
Rather than build one large platform or pursue a single industry vertical, the founding team chose a different path — three focused service areas that address specific, recurring pain points in how organisations handle information, data, and process coordination. Each service was shaped through early client engagements, refined through feedback, and kept deliberately scoped so results could be evaluated honestly.
Today, Infera works with businesses across Singapore and the broader Asia-Pacific region — from mid-size companies managing internal knowledge bases, to organisations with sensitive datasets that require careful synthetic handling, to operations teams dealing with coordination complexity that manual tools can no longer keep up with.
Our approach has remained consistent: understand the environment first, design to fit it, and measure whether the work made a difference. We're not interested in adding AI for its own sake — only in cases where it genuinely simplifies something that was previously difficult.
Founded in Singapore
Engagements completed
Focused service areas
What we're here to do
"Make AI useful — not just interesting. Our work is done when your team is operating more effectively than before, with tools they understand and own."
Transparency
We explain what we're building, why we're building it, and how to evaluate whether it's working.
Practicality
We favour approaches that fit your constraints over ones that require you to redesign your organisation around them.
Responsibility
Data handling, privacy, and the downstream effects of automation are taken seriously in every engagement.
Measurability
We build in evaluation from the start so that progress can be observed, not just claimed.
People behind the work
Jonathan Tan
Co-founder & Head of Engineering
Background in distributed systems and semantic retrieval. Leads technical architecture for search and data services.
Priya Lakshmanan
Co-founder & Head of Data Science
Specialises in generative modelling and privacy-preserving data techniques. Leads the synthetic data practice.
Marcus Ho
Head of Delivery & Client Success
Manages engagement design, integration planning, and the post-deployment refinement process for workflow clients.
How we hold ourselves accountable
Data Protection
We follow Singapore's PDPA framework across all engagements. Data handling protocols are documented and agreed upon before any work begins.
Evaluation First
Every deliverable includes a testing or measurement component. We don't consider an engagement complete until there's a way to assess the difference made.
Version-Controlled Delivery
All code, scripts, and configuration delivered to clients are version-controlled and documented so teams can trace and understand every component.
Defined Scopes
We don't start work without a written scope. Clients know what is included, what is excluded, and what the timeline looks like before a project begins.
Post-Delivery Support
For workflow engagements, a 90-day refinement period is built in. For other services, we offer structured handover sessions and documentation review.
Confidentiality
Non-disclosure agreements are standard on all engagements. Client data and business context are kept strictly confidential throughout and after the engagement.
AI consulting in Singapore — what working with Infera looks like
Infera operates from CapitaGreen in the heart of Singapore's financial district, working with organisations across a range of sectors — from financial services and logistics to healthcare administration and professional services. The common thread across our client base isn't industry — it's a specific kind of problem: information that's hard to surface, data that can't be used in its current form, or processes that require too much manual coordination to scale sensibly.
Our engagements are typically four to twelve weeks in duration, depending on service complexity. We work closely with your internal teams throughout — not as external consultants who hand over a black box, but as collaborators who build something your team will understand, own, and be able to continue developing. This is particularly important for organisations in Singapore navigating the PDPA and sector-specific data governance requirements.
Search Enhancement, Synthetic Data Generation, and Workflow Orchestration are not abstract capabilities — they're responses to problems we've encountered repeatedly. The services are scoped to be completable, evaluable, and genuinely transferable to your team. If you're weighing whether AI is the right tool for a particular challenge your organisation is facing, we're happy to have that conversation without any obligation on your part.