Custom Solutions

From raw data to
production deployment

We build custom digital and AI systems end-to-end — from validating your data through training models, writing production code, designing interfaces, testing every layer, and deploying into your environment.

01

Data Validation

Every project starts with understanding what you have. We profile, validate, and assess your source data so the build starts from a clean foundation — not assumptions.

Schema checks, null analysis, distribution profiling, and anomaly detection across your datasets before a single line of product code is written.

Data Profiler

radiology_studies.csv
24,847 rows|7 columns
5/7
Schema Valid
96.3%
Completeness
3
Anomalies
Needs review
Ready
ColumnTypeNullsCompletenessStatus
patient_idUUID0%
100%
Pass
scan_dateDATE0.2%
99.8%
Pass
modalityENUM0%
100%
Pass
report_textTEXT12.4%
87.6%
Review
diagnosis_codeVARCHAR3.1%
96.9%
Pass
referring_physicianVARCHAR8.7%
91.3%
Review
priority_levelINT0%
100%
Pass
Anomaly Detected

report_texthas 12.4% null rate — exceeds 5% threshold. 47 rows contain truncated values (<10 chars). Recommend source review before model training.

02

AI Training

Models are trained against your real-world data, tuned for your domain, and validated against clinical or operational benchmarks that matter to your team.

Experiment tracking, hyperparameter tuning, cross-validation, and performance benchmarking — all before anything reaches production.

Experiment Tracker

Training
exp-047 running
Active: RadBERT-v3Epoch 24/30
Train lossVal loss
94.2%
Accuracy
0.931
F1 Score
0.087
Train Loss
0.102
Val Loss
Experiment History
exp-047RadBERT-v394.2%0.93124/30Best
exp-046RadBERT-v291.8%0.90430/30Done
exp-045ClinicalT589.1%0.87630/30Done
exp-044Baseline-LR76.3%0.741Done
03

Software Development

Production-grade architecture from day one. APIs, services, and data pipelines are built with iterative releases so you see working software early and often.

TypeScript, Python, cloud-native infrastructure, CI/CD pipelines, and automated deployments — built for scale and maintainability.

Deployment Pipeline

v2.4.1 shipping
Build
Test
Staging
Approval
Production
api/routes/studies.ts
export async function
getStudies(req: Request) {
// Validate & authorize
const user = await auth(req);
const filters = parseFilters(req);
 
// Query with pagination
const studies = await db.study
.findMany({
where: buildWhere(filters),
include: { aiResults: true },
take: filters.limit,
});
 
return
Response.json({ studies });
}
Recent Commits
KR
Add study pagination + cursor support
a3f2c1d2m ago
JL
Implement AI result caching layer
e8b1a4f28m ago
KR
Add RBAC middleware for study routes
c2d9f3b1h ago
AS
Setup DICOM ingestion pipeline
7a4e2c12h ago
JL
Configure staging environment
f1b3d8e3h ago
KR
Add audit logging to all endpoints
9c2a7f44h ago
04

UI/UX Design

Interfaces designed around your users' actual workflows. Every screen is purposeful — built to reduce cognitive load and accelerate decision-making.

Design systems, component libraries, accessibility-first patterns, and responsive layouts tailored to clinical and operational contexts.

Design System

v1.2
DesktopMobile
Components
Button6
Card4
DataTable3
Modal2
StatusBadge5
Sidebar2
Chart4
Form8
DataTable/Clinical Studies
PatientStudyPriorityAI ScoreStatus
M. TorresCT HeadUrgent
0.94
Flagged
J. ParkMRI KneeRoutine
0.23
Normal
S. WilliamsCT ChestRoutine
0.67
Review
R. KimUS AbdoUrgent
0.88
Flagged
L. ChenMRI BrainStat
0.91
Flagged
Tokens
05

Testing & QA

Comprehensive validation across every layer. Unit tests, integration tests, and end-to-end workflows are verified before anything reaches your users.

Automated test suites, regression testing, performance benchmarks, and compliance validation — nothing ships without passing the bar.

Test Runner

All passing
14/14 passed in 4.8s
4
Suites
14
Tests
94.7%
Coverage
4.8s
Duration
API Routes4 tests
GET /studies returns paginated results12ms
POST /studies validates required fields8ms
RBAC blocks unauthorized access15ms
Rate limiting triggers at threshold45ms
AI Pipeline4 tests
Model inference returns valid scores234ms
Batch processing handles 1k+ studies1.2s
Fallback activates on model timeout67ms
Confidence thresholds flag correctly18ms
E2E Workflows3 tests
Study upload → AI triage → review3.4s
Login → dashboard → export report2.1s
Admin creates user with RBAC roles1.8s
Compliance3 tests
Audit log captures all write ops22ms
PII fields encrypted at rest9ms
Session timeout enforced at 30min31ms
06

Integration & Deployment

The final mile: connecting into your existing systems, migrating data, and rolling out with monitoring and support plans already in place.

API integrations, SSO, RBAC, audit logging, health checks, and observability — deployed with confidence into regulated environments.

System Status

All systems operational
Last checked: 30s ago
External Integrations
PACS Server
Latency: 12ms
Connected
EHR / EMR
Latency: 45ms
Connected
Auth (SSO)
Latency: 8ms
Connected
DICOM Gateway
Latency: 23ms
Connected
Audit Logger
Latency: 3ms
Active
Service Health
API Gateway99.97%
AI Inference99.94%
Database99.99%
Object Store99.99%
Cache Layer99.98%
Active Deployment
Versionv2.4.1
Regionca-central-1
Deployed12 min ago
RollbackAvailable
Rapid Prototype Development

Build, validate, and ship
a working prototype fast

We turn initial requirements into a production-style prototype in short cycles, so decisions are grounded in real product behavior — not slides and assumptions.

01
2–3 days

Discovery Sprint

We sit down with your team to define the problem space, map constraints, integration boundaries, and success criteria — so the prototype solves the right problem from day one.

Stakeholder interviews, workflow mapping, data landscape review, and a prioritized requirements document delivered before any code is written.

Discovery BoardDay 2 of 3
4 sessions · 2 complete
Stakeholder Sessions
Radiology Lead
Triage workflows, pain points
45 minComplete
IT Director
Integration landscape, security
30 minComplete
Clinical Manager
Staffing, scheduling gaps
40 minIn progress
Compliance Officer
PIPEDA, audit requirements
30 minScheduled
Constraints
PACS vendor
Intelerad
Hosting
On-premise required
Auth
SAML SSO (existing)
Timeline
MVP in 6 weeks
02
1 week

Clickable Prototype

High-fidelity interaction models for stakeholder review — real screens, real workflows, real feedback. Not wireframes or slide decks, but something your team can click through and critique.

Figma-to-code design system, responsive layouts, interactive state management, and clinician-facing UI patterns purpose-built for healthcare contexts.

Prototype Previewv0.3 · Review ready
DesktopMobile
Screens
Dashboard
Study Queue
AI Results
Settings
Reports
User Mgmt
AI Results/Study Detail View
PatientStudyAI FindingScore
M. TorresCT HeadHemorrhage suspected0.96
L. ChenMRI BrainMass effect0.91
R. KimCT ChestEffusion noted0.83
Design tokens
03
3–5 days

Technical Spike

Validate the hard parts before committing to a full build: data contract viability, API feasibility, model inference latency, and integration risk factors.

Proof-of-concept integrations, performance benchmarks, security assessment, and a technical risk log with mitigation strategies.

Technical Spike1 risk flagged
5 checks · 4 passed
Feasibility Checks
PACS DICOM query latency23ms avg
AI inference throughput< 500ms / study
SSO token validationValid flow confirmed
Data volume stress test10k studies OK
On-prem network egressBlocked — needs VPN
Risk Log
Network egress blocked for AI inferenceHigh
VPN tunnel or edge deployment
Legacy HL7 format on referral dataMed
Custom parser + fallback
No staging environment availableLow
Containerized test env
04
2–4 weeks

Pilot Build

A production-style MVP with the architecture, security patterns, and deployment readiness to move directly into a pilot — not a throwaway demo.

Production-grade code, CI/CD pipeline, RBAC, audit logging, health monitoring, and a deployment checklist for your environment.

Pilot DeploymentSprint 4 · Shipping
Build
Test
Staging
Approval
Production
Deployment Checklist
RBAC roles configured
Audit logging verified
Health checks passing
SSO integration tested
Data migration complete
Rollback plan documented
Monitoring dashboards live
Environment
Versionv1.0.0-pilot
Regionca-central-1
TypeOn-premise
DeployedJust now
RollbackAvailable
Pilot Scope

2 core workflows activated

12 pilot users onboarded

Weekly feedback cadence set

< 5 days

Avg. kickoff

From first call to active sprint

2 core flows

Pilot scope

Focused on highest-impact workflows

Weekly

Feedback loops

Stakeholder reviews built into every cycle

Sprint-based

Delivery mode

Iterative releases, not big-bang launches

Need an end-to-end partner for custom delivery?

From architecture to deployment, we build and integrate software around your operational reality.

Get in touch