Case Study
Building the Platform Behind
Synthetic Intelligence.
How Be Data Solutions embedded specialist engineering talent to help an AI-powered consumer insights startup build, ship, and scale a platform that replaces traditional market research — in seconds.
2
Specialists Deployed
2
Distinct Roles
AI
Platform Focus
100x
Faster than Surveys
Client Overview
An AI-Powered Consumer Intelligence Platform
Be Data Solutions was engaged by a London-based AI startup building synthetic audience technology — enabling brands and enterprises to simulate how their customers will think, feel, and react, in seconds rather than weeks.
The client has developed a scientifically rigorous platform that constructs digital simulations of a brand’s target audience using a combination of large language models, traditional machine learning, and behavioural social science. Companies feed in existing survey or focus group data, and the platform builds a synthetic population that can be queried instantly — delivering survey-level accuracy at a fraction of the time and cost.
The platform powers use cases across product development, campaign testing, message optimisation, new market exploration, and audience discovery — and has been validated against millions of real-world responses with accuracy exceeding 90% NDAM. The business was co-founded by former senior advisors to the UK Government, including the former Chief Data Advisor to the Prime Minister.
As the platform moved from proof-of-concept to commercial product, the client required specialist engineering and quality assurance talent to help build and validate the core technology with the rigour its science-backed positioning demanded.
Business Need
Engineering an AI Platform That Has to Be Right
For a platform whose entire value proposition rests on scientific precision, the bar for engineering quality and testing rigour is exceptionally high.
Senior Full-Stack Engineering
The platform required senior-calibre full-stack engineering to build the product interfaces, API layer, and integration infrastructure that make complex ML predictions accessible to enterprise users.
Uncompromising Quality Assurance
A platform that positions itself on scientific accuracy cannot tolerate software defects. A Senior SQA Engineer was essential to enforce quality standards across every release of a product that enterprises stake real decisions on.
Startup Pace, Enterprise Standards
The client needed talent that could move at startup velocity while delivering the code quality, test coverage, and reliability that large enterprise clients and science-led credibility demands.
Efficient Scaling
As an early-stage company, the client needed specialist resource delivered with precision — the right roles, the right seniority — without the cost and delay of building a full in-house team from scratch.
Augmented Team
Precision Talent for a Precision Platform.
Be Data Solutions deployed two senior specialists — each chosen for their ability to operate at the intersection of technical excellence and fast-moving product delivery.
Senior Full Stack Developer (1x)
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- Designed and built the core product interfaces and application layer enabling enterprise users to define, query, and interact with synthetic audience simulations in real time.
- Developed robust RESTful APIs and backend services integrating the client’s machine learning prediction engine with front-end product surfaces and third-party data inputs.
- Built scalable, maintainable full-stack features using modern JavaScript frameworks (React / Next.js) with Node.js / Python back-end services — optimised for low-latency AI-powered query responses.
- Collaborated closely with the client’s data science team to ensure engineering implementations faithfully reflected the underlying statistical and ML model outputs.
- Implemented secure data handling, authentication, and permission logic for enterprise customer environments with varying levels of data sensitivity.
- Contributed to architectural decisions, code standards, and documentation, providing senior-level technical judgement within a lean, fast-moving engineering team.
- Participated in sprint ceremonies, product reviews, and cross-functional planning sessions — driving delivery aligned to the client’s commercial roadmap.
- Owned the end-to-end quality assurance strategy for a science-led AI platform where accuracy, consistency, and reliability are core product differentiators — not optional extras.
- Designed and executed comprehensive test plans covering functional correctness, regression testing, API contract validation, and edge-case handling across the platform’s AI query pipeline.
- Built and maintained automated test suites to accelerate release velocity without sacrificing quality — enabling the team to ship with confidence across rapid iteration cycles.
- Validated that platform outputs — synthetic audience predictions and statistical distributions — behaved consistently with expected model accuracy, in close collaboration with the data science team.
- Defined and enforced quality gates within the CI/CD pipeline, ensuring no defective code reached production or enterprise client environments.
- Documented test coverage reports, defect logs, and release quality summaries — providing transparency to product leadership on platform readiness ahead of each release.
- Worked closely with the Senior Full Stack Developer to identify and resolve issues at the code level early — reducing rework and maintaining high development velocity.
Approach
Embedded, Agile, Accountable
Both specialists operated as genuine team members — integrated into the client’s engineering culture, tooling, and delivery rhythm from the start.
Fully Embedded Teams
Both engineers were integrated directly into the client’s agile workflow — sprint planning, daily stand-ups, demos, and retrospectives included.
UK Time Zone Aligned
Remote-first delivery structured around UK working hours, enabling real-time collaboration with the client’s London-based founders and product team.
Science-Aligned Engineering
The team worked in close coordination with the client’s data scientists — ensuring engineering implementations preserved the statistical integrity the product is known for.
Quality at Every Stage
SQA was embedded across the full delivery lifecycle — not bolted on at the end — protecting every release from defects and maintaining platform credibility.
Impact Delivered
The Right Two People Made the Difference.
In a lean startup, every role compounds. Two senior specialists drove outsized impact across product velocity, reliability, and commercial credibility.
Faster Product Velocity
Senior engineering capability enabled the client to build and ship platform features at pace — critical for an early-stage company competing on speed-to-market in the AI insights space.
Credibility-Preserving Quality
Dedicated SQA engineering ensured the platform’s outputs met the accuracy standards the client’s scientific positioning requires — protecting enterprise client confidence and brand integrity.
Seamless Team Integration
Both specialists integrated into the client’s small, high-trust team without friction — contributing beyond their remit to architectural and product thinking as well as core delivery.
Lean, Efficient Resource Model
Two precision hires delivered the capability of a much larger team — at a cost model that suited an early-stage company with institutional backing and commercial ambition.
Cost Efficiency
Senior specialist talent at a fraction of equivalent UK permanent hiring cost — with no recruitment overhead or HR burden.
Zero Onboarding Lag
Both engineers were productive immediately, embedding into existing tooling and processes without disrupting delivery momentum.
Sustained Oversight
Be Data Solutions maintained ongoing performance oversight throughout the engagement, ensuring consistent quality and proactive issue resolution.
Flexible Engagement
Resourcing was available on adaptable terms aligned to the startup’s evolving funding and roadmap priorities.
