Fixed-scope packages
Best for
A clear first step, a defined deliverable or a donor-funded activity.
How it works
Agreed scope, timeline, deliverables and price before work starts.
Perspective
Here is how we see the next few years, and how we have built MekongSI to stay useful as AI keeps moving.
Six shifts
Shift 01
Reporting, document handling and first-line analysis are increasingly done by software, supervised by people.
How we respond
We redesign workflows for human + AI teams and build agents with human review, audit trails and clear accountability.
Shift 02
Most organisations have tried AI; few have it working reliably inside real processes.
How we respond
Senior, forward-deployed teams who sit with your people, integrate with your systems and stay until it works in production.
Shift 03
Boards, donors and regulators now ask for evidence that AI is safe, fair and under control.
How we respond
Governance, independent evaluation and managed AI operations as standard services, not afterthoughts.
Shift 04
Sensitive data must stay in-country or on-premises, and AI must work in local languages.
How we respond
Model-agnostic architectures that run in your cloud, on-premises or in-region, with Khmer and English in scope.
Shift 05
Buyers increasingly expect to pay for defined deliverables and results rather than time spent.
How we respond
Fixed-scope packages, managed capacity and outcome-based partnerships where baselines are clear.
Shift 06
Every role is changing; organisations need people who can direct, check and improve AI.
How we respond
DataU training and capacity transfer built into engagements, so capability stays with you.
The organisation of the future
As AI agents take on routine analysis, reporting and document work, organisations will run with fewer hand-offs and smaller teams, each directing a set of AI tools. The scarce skills become judgement, domain knowledge and the ability to check and improve what AI produces.
That makes three things decisive: data that AI can trust, governance that lets people move quickly without adding risk, and teams trained to work this way. Those are exactly the capabilities we build with clients, through transformation, data and AI engineering, governance and DataU training.
For development organisations and governments, it also means doing more with constrained budgets while protecting the people behind the data, which is why responsible AI and data sovereignty sit at the centre of how we work.
Built to last
Architectures that can swap in better or cheaper models as they arrive, without rebuilding the system.
Every AI system ships with evaluations that are re-run when models, prompts or data change.
AI supports our research and engineering under the same data-protection rules as everything we do: faster turnaround, with confidentiality intact.
Consultants and engineers who frame the right problems, check what AI produces and take responsibility for results.
Ways to engage
We price around deliverables, capacity and outcomes, so you pay for results rather than time.
Best for
A clear first step, a defined deliverable or a donor-funded activity.
How it works
Agreed scope, timeline, deliverables and price before work starts.
Best for
Multi-phase transformation, platforms, and ongoing data and AI work.
How it works
Phased delivery, or a dedicated pod billed monthly against agreed capacity and service levels.
Best for
Repeatable processes with a measurable baseline, such as report production or document processing.
How it works
Fees linked to agreed results, after a baseline is measured together.
See how this translates into our services.
Tell us what you are trying to achieve. We will tell you honestly whether we are the right partner, and what a sensible first step looks like.