Scope we cover end to endAI & LLM SystemsAlgorithms & Data IntelligenceWeb & Mobile ProductsCloud ArchitectureFirmware & Connected DevicesManufacturing & Commercialization
A veterinary-specific system that interprets companion-animal time-series health data, owner-reported symptoms, and medical imaging into structured clinical support.
Scope
Algorithm · Model tuning · Product
Stack
Cloudflare · Standalone LLM
Time-series-based interpretation algorithm — design and implementation
AI-powered clinical consultation and intake system for evaluating the clinical condition of companion animals
Analytical engines for dermatological and ophthalmological symptoms — design, development, and deployment
X-ray interpretation algorithm and analytical engine
Fine-tuning of LLMs on open datasets and veterinary X-ray datasets
Veterinary-specialized AI interpretation system built on those fine-tuned models and proprietary analytical algorithms
Data-Grounded Persona and Market Intelligence Platform
A market-analysis system that normalizes external statistical, economic, and market data into anchor sets, then generates synthetic personas and focus-group discussions from them.
Scope
Data pipeline · Algorithm · App
Output
Personas · Synthetic FGD
Collection and analysis of data from 167 international organizations, national statistical agencies, banks, economic institutions, and marketing research organizations
Mathematical algorithms for persona generation — design and implementation
Integration of the persona-generation algorithms with LLMs to produce data-grounded synthetic personas
System architecture, FGD framework, and market-analysis workflow
Note — Veterinary analysis is a support system and does not replace diagnosis by a licensed veterinarian. Synthetic personas and FGD support hypothesis forming and do not fully replace real consumer research.
What we build
Six capabilities, one delivery team.
01
AI & LLM Systems
Domain-grounded AI systems with retrieval, tools, and human review.
02
Algorithms & Data Intelligence
Time-series, statistical, and scoring engines whose reasoning can be traced.
03
Web & Mobile Products
Responsive web and native apps with multilingual UI and role-based views.
04
Cloud Architecture
Edge-first APIs, data stores, and monitoring sized to real traffic.
05
Firmware & Connected Devices
Sensor firmware, BLE and Wi-Fi data paths, and app integration as one flow.
06
Manufacturing & Commercialization
PCB, SMD, tooling, and assembly handoff reviewed for manufacturability.
Integrated engineering
One chain from the business problem to a running service.
Most vendors own one link. We design the whole chain, which is why the data model, the algorithm and the device stay consistent with each other.
01
Business Problem
02
Data & Domain Logic
03
Algorithms & AI
04
Cloud & APIs
05
Web / Mobile
06
Device / Firmware
07
Deployment & Scale
Why Projectkhen
Evidence before adjectives.
01
Analysis grounded in real data
External and domain data is collected and structured before any modelling, so a result can be traced back to its source.
02
Proprietary algorithms combined with LLMs
Mathematical and statistical models carry the reasoning. Language models handle interpretation and interaction on top of them.
03
One team from software to device
Data, AI, cloud, web, mobile, and firmware are designed together instead of being handed between separate vendors.
04
Scoped around MVP and running cost
The first release is sized to what proves the value, and infrastructure is planned against the load you actually expect.
05
Experience in medical, veterinary, and manufacturing work
Time-series and imaging analysis, regulated-domain constraints, and the path from prototype to production hardware.
We do not publish fixed development fees, because cost moves with how ready your data is, whether the industry is regulated, how many external systems are involved, which AI models are needed, whether hardware is in scope, and the operating scale. The assessment form asks for a budget range so the proposal we come back with is realistic rather than aspirational.
What happens after I send the form?
Four steps. You get an automatic confirmation that it arrived. We read what you sent. We come back with follow-up questions or a proposed scope. If detail needs protecting, we agree an NDA before going further. The confirmation is a receipt, not an acceptance.
Can we sign an NDA before I explain the project?
Yes. Keep the form to a general outline and say that an NDA is needed. We sign one first and pick up the detail afterwards. Do not attach sensitive source material before that is in place.
We are not in your time zone. How does that work?
Everything runs over email, which is why we do not schedule calls. Questions, scope, and documents stay in one thread that both sides can read at a workable hour. Tell us your country and time zone in the form and we plan delivery around them.
I have an idea but no data and no system yet.
That is where Discovery and Feasibility exist. The first stages clarify the problem, check whether the data and models can carry it, and say where the technical risk sits — before anyone commits to building.
Can you work on a system we already run?
Yes. One of the engagement models is an analysis of what you run today followed by targeted extension. Another is advisory and architecture review without taking over delivery.
Tell us what you need to build.
Share your idea, current system, available data, target schedule, and budget range. We will review the technical scope before proposing the next step.
