Insights from our conversation with Rahul Joshi and Gaurav Chouhan, CEO and CTO of Techdome.

In the high-stakes world of clinical trials, data inconsistencies aren’t just annoying — they can derail outcomes. Our team spoke to the leaders at Techdome, a data-focused technology company, who faced this head-on while helping build data automation solutions for a clinical trial involving 80,000 participants.

“We encountered records with birth years from the 1700s and 1800s. One record could have eight or nine anomalies.”

What followed was a deep and systematic overhaul of how data collection was visualized, structured, validated, and integrated — turning a chaotic process into a resilient data pipeline that led to the elimination of 95% of human errors through automation of manual processes.

WATCH: How Techdome handled problems with data handling for clinical trials.

The challenge: Human-centered systems with human-sized errors

Clinical trials are “inherently time-intensive and heavily human-driven,” as Joshi put it. That human element introduced critical challenges:

• Inconsistent formatting of patient names, dates, and records
• Invalid data entry, such as impossible birthdates or approximations
• Sample spoilage due to process breakdowns
• Feedback gaps, like patients forgetting to report medication intake
• Legacy system limitations, such as multi-line text file formats

Each of these could break the flow of a trial — and at scale, the cumulative damage was enormous.

The strategy: How Techdome tamed the chaos

Map the data flow from day zero. Techdome kicked off projects with a “day-zero diagram” using Miro or Lucidcharts to visually define data flows.

“We brainstormed to identify key entities — patients, lab technicians — and their attributes.”

This helped the team understand what data mattered most and where problems could emerge.

Break down processes into modular components. Instead of managing everything as a single flow, Techdome split workflows into smaller, role-specific units like recruitment, history tracking, and follow-ups.

“This modular approach helped us streamline operations and address inefficiencies effectively.”

Validate data rigorously and in real time. Validation happened at every level:

• Strict constraints on data types and indexes
• Predictive validation UI that flagged anomalies in green, orange, or red during entry
• Flagging layers that assigned weight to attributes (e.g., instrument, location, demographics)

“When entering a patient’s details like age, region, height, or weight, the system provides predictive values. If an entry deviates significantly, it flags it in green, orange, or red. This discourages manipulation since flagged entries are subject to review.”

Match the right database to the right data. They used relational databases for structured data with defined relationships (e.g., patient-to-trial) and NoSQL for messy, dynamic forms.

"In clinical trials, parameters can change based on the trial type. For example, one trial might prioritize HDL levels, while another focuses on sugar levels or other metrics. SQL struggles with accommodating such dynamic attributes effectively, so we use NoSQL for the first layer to handle these attributes."

Dynamically handle third-party integrations. External systems often sent CSVs with shifting columns. Techdome used NoSQL loops to adapt APIs and categorize the data in real time.

“We faced integration challenges with third-party systems. Some providers had consistent data formats, but others sent dynamic columns in their exports, causing runtime issues. To handle this, we used a NoSQL loop to dynamically process the CSV data and adapt APIs accordingly."

Standardize legacy formats without rebuilding. Many records came from older systems storing values across multiple lines. Techdome created converters to handle this input and standardize it.

“Legacy systems often use outdated formats like line-by-line text files for patient records. For example, a record might store a patient’s name, age, and health data across multiple rows instead of a single entry with columns. Handling these dynamic rows and columns is challenging, but we addressed it effectively by brainstorming and implementing solutions to standardize the data."

Enhance communication to boost engagement. They added email updates, automated notifications, and 6–12-hour interval reports to keep patients and staff aligned.

“We improved efficiency through better communication systems—email updates, notifications, and reports at regular intervals (e.g., 6-hour, 8-hour, or 12-hour updates). This ensured data anomalies could be tracked and controlled in real-time."

They also focused on simplicity for healthcare professionals who weren’t always tech-savvy.

Prepare for instrumental and human error. The system flagged outliers based on pattern detection — whether caused by device misreadings or human approximations.

“Some data points also need to be invalidated. For instance, a 20–30% variation in a reading within a week could indicate human or instrumental error. Such anomalies must be flagged automatically to maintain data integrity and avoid skewing the study."

Build in a continuous feedback loop.

"We had a continuous feedback loop during data modeling. It was iterative and adaptive to learn from errors in production."

Errors in production weren’t fatal — they were fuel. Techdome trained their system to log, learn from, and correct issues dynamically.

The takeaway: Trustworthy AI begins with bulletproof data

Techdome’s approach was foundational. Their success in clinical trials didn’t come from any one technology, but from a rigorous, iterative commitment to making messy, real-world data usable and trustworthy.

If you're working in a regulated, human-driven space — healthcare, finance, government — this is your blueprint:

• Start with clear visual maps
• Match tools to data realities
• Validate before you aggregate
• Don’t ignore edge cases — log them, learn from them

And most of all: respect the chaos, or it will wreck your pipeline.