01
Artificial Intelligence
AI that fits the workflow
AI strategy, model evaluation, custom NLP and computer vision, retrieval systems, and decision support tools designed around real work, not demo behavior.
Built for operational problems
Systems for teams that have to get the work right.
KOD 201 designs and delivers data platforms, AI products, field tools, and health informatics systems for organizations that cannot afford vague work, brittle software, or reports that arrive too late to matter.
Delivery systems online
What KOD 201 builds
Connected reporting, decision support, field capture, automation, analytics, and the operating structure around them.
Bias for action
Clear architecture, usable software, documentation that people can follow, and delivery choices grounded in real constraints.
Bias for ownership
Transfer matters. Teams should be stronger after the project, not more dependent on the vendor.
KOD 201 works across public health, research, higher education, medtech, and other operating environments where data quality, workflow design, and reliable systems are not optional.
Data platforms
Warehousing, pipelines, reporting, governance
AI systems
NLP, computer vision, model evaluation, workflow tools
Field operations
Offline-first mobile, capture tools, edge deployments
Research delivery
Study operations, analysis, technical writing, translation to practice
About
KOD 201 is named after the HTTP 201 status code, created. The name reflects the standard we care about. Make something useful. Make it real. Make it ready to be used under pressure, not only admired in a pitch deck.
We help organizations solve operational problems with better data systems, stronger workflows, well-scoped AI, and technical delivery that holds up after launch.
Vision
More teams use data and AI with discipline, speed, and measurable operational value, not as a branding exercise.
Mission
Design, build, and transfer systems people can run. That includes architecture, delivery, training, documentation, and the less glamorous details that make systems stick.
Services
Five service areas, one working style. Clear scope. Strong implementation. Clean documentation. Practical handoff.
02
Data
Data systems that stay trustworthy
Data engineering, warehousing, data models, governance, analytics pipelines, and reporting structures that reduce manual work and improve decision speed.
03
Systems engineering
Products, platforms, and connected systems
Full-stack product delivery, interoperability, DevOps, cloud deployment, edge systems, and the glue work required to make systems talk to each other.
04
Applied research
Research support with delivery discipline
Study design support, implementation workflows, reproducible analysis, technical writing, and translation of findings into software, programs, or operating guidance.
05
Optimization
Program and business improvement
Operational redesign across people, process, and technology, with targeted training for AI adoption, governance, routine use, and internal capability growth.
06
Engagement style
Delivery with a steady hand
Turn-key builds, managed technical support, research partnerships, and embedded collaboration for organizations that need both technical depth and operational follow-through.
Solution areas
Examples of the kinds of systems we build. These are not product slogans. They are categories of work with clear operating value.
01
AI labs
Applied environments for model development, testing, evaluation, and iteration by real teams.
02
Health systems
Clinical platforms, surveillance tools, reporting layers, and interoperability for care and program operations.
03
Data platforms
Pipelines, warehouses, shared models, and analytics structures that improve trust in the numbers.
04
Research tools
Capture, monitoring, analysis, and dissemination tooling for teams moving between research and practice.
05
Workflow AI
LLM and automation systems that sit inside existing workflows instead of asking staff to invent new ones.
06
Responsible AI
Governance, auditability, privacy controls, and implementation choices that reduce avoidable risk.
07
Mobile and edge
Offline-first mobile tools and edge deployments for field settings, low-connectivity environments, and distributed operations.
08
Capability building
Training, operating guidance, and structured handoff so client teams can run the work after delivery.
Process
The working model is simple. Understand the problem. Make the technical choices explicit. Build carefully. Roll out in a controlled way. Transfer knowledge before the team becomes dependent on us.
Phase 01
Discover
Define the problem, users, constraints, success measures, and what has already failed.
Phase 02
Design
Translate the problem into architecture, workflow design, scope, resourcing, and delivery choices.
Phase 03
Build
Develop the system, test the details, and remove avoidable points of failure before rollout.
Phase 04
Deploy
Launch in stages, connect to existing systems, monitor behavior, and support operational adoption.
Phase 05
Transfer
Document the work, train the owners, and leave the client with something they can run.
Selected work
A few examples of the kind of work KOD 201 is built to do. These are framed around the operating problem, not the technology alone.
Public health
Live HIV program reporting across more than 50 clinics
A national linkage program relied on manual reporting with a six-week lag. That made timely follow-up difficult and kept program teams working from stale information.
We designed a live data pipeline and dashboard layer that connected distributed sites, reduced manual handling, and gave managers visibility quickly enough to act on it.
ResultReporting turnaround reduced from six weeks to 24 hours.
Higher education
Faculty AI adoption tied to real research workflows
Researchers were interested in AI, but most had no practical starting point and little time for abstract experimentation.
We built a hands-on training program around actual research tasks, sample workflows, and usable examples. The emphasis was task fit, not theater.
ResultPilot participation increased by 40 percent.
Medtech
Smart pill-dispensing prototype with device and cloud integration
A medtech client needed a working prototype that combined embedded logic, software control, and secure communication across the device and cloud layers.
We delivered the prototype, integrated the system end to end, and produced something the client could validate, test, and refine instead of merely describing.
ResultWorking prototype delivered for validation and next-stage iteration.
Why teams hire us
Not every client needs the same thing. The pattern is usually some combination of technical depth, delivery structure, and confidence that the work will not fall apart under real conditions.
We are comfortable with messy environments
Disconnected systems, uneven data quality, mixed stakeholder expectations, and field constraints are normal conditions, not edge cases.
We care about the operating model
Architecture matters. Training matters. Documentation matters. Governance matters. The work is more than code.
We aim to leave teams stronger
The best outcome is not permanent vendor dependence. It is a client with a better system and more internal capability than before.
Start here
Have work that needs more than a nice deck?
KOD 201 works on serious problems in data, AI, software, and operational systems. When the work matters, the details matter too.