SERVICES
From raw data to
working AI systems.
Hands-on engineering — not advisory decks. Every engagement ends with something built, tested, and running.
WHAT TASPRA BUILDS
Six things. All done properly.
Each service maps to a specific type of problem. If your problem is on this list, it can be solved. If it isn’t, let’s talk — the answer might still be yes.
Data Pipeline Engineering
Building and rebuilding data pipelines that actually hold up at scale — distributed ingestion, transformation, storage, and delivery. Designed for schema evolution, operational reliability, and the failure cases that always show up in production.
- PySpark-based distributed pipeline design and implementation
- Delta Lake / lakehouse architecture migration from legacy batch
- Real-time streaming with Kafka + Spark Structured Streaming
- Schema evolution handling — pipelines that don’t break on upstream changes
- Data quality validation and automated alerting
- Pipeline monitoring, observability, and incident runbooks
- Cost and performance optimisation of existing pipelines
- Technical documentation and handoff for in-house teams
ML Engineering & Deployment
Production ML systems that generate business value, not notebook experiments that never leave a Jupyter session. Model training, feature pipelines, and deployment wired into actual business workflows.
- Feature engineering from real business data
- Model training and evaluation pipelines with PySpark ML
- Churn prediction, demand forecasting, anomaly detection
- Model output piped into CRM, reporting, or alerting systems
- Regular model refresh and drift monitoring
LLM & RAG System Design
AI applications built for contexts where hallucination is not acceptable — billing data, financial documents, compliance content, technical knowledge bases. Grounding, evaluation, and fallback logic included.
- RAG systems over private documents and knowledge bases
- Prompt engineering, grounding, and output validation
- LLM fine-tuning for domain-specific language
- Evaluation frameworks — measuring accuracy, not just vibes
- Multi-step AI agent workflows with tool use and memory
Intelligent Business Automation
Automating the workflows that eat hours every week — reporting, data movement, alerting, document processing. Reliable automation that runs unattended, handles edge cases, and alerts when something actually needs a human.
- Automated report generation — SQL to narrative to email delivery
- Document processing and data extraction pipelines
- API integrations connecting internal systems
- Scheduled data jobs with proper error handling and retry logic
- Notification and alerting systems tied to real business thresholds
AI-Powered Product Development
Building software products with AI at the core — not AI bolted on as an afterthought. From early-stage idea to working product, with the data infrastructure, AI integration, and delivery layer all done properly.
- Product scoping and technical architecture for AI-first products
- End-to-end build: backend, AI layer, frontend, deployment
- Digital product platforms with e-commerce and delivery (WooCommerce)
- SaaS MVP development and launch infrastructure
- Ongoing technical co-founding / fractional CTO for early-stage teams
AI & Data Strategy Consulting
Honest assessments — not vendor-aligned recommendations. Useful for teams trying to figure out where AI actually helps, what to build vs buy, and how to structure a data function that doesn’t become a bottleneck.
- AI readiness assessment — what you have, what you need, what to do first
- Data platform audit and modernisation roadmap
- Build vs buy analysis for specific AI/data tooling decisions
- Engineering team mentoring and technical leadership support
- Due diligence on AI/data capabilities for investment decisions
HOW IT WORKS
Simple process. No surprises.
Every engagement follows the same structure — clear scope, regular check-ins, and nothing shipped without being tested.
Scoping call
A 30–45 minute conversation to understand the actual problem — not a sales pitch. We figure out if there’s a fit and what a reasonable scope looks like.
Proposal & agreement
A written proposal with clear deliverables, timeline, and price. No hidden costs, no scope that expands without a conversation about it.
Build & deliver
Regular updates as work progresses, not silence until delivery day. Final handoff includes documentation and a working session if needed.
ENGAGEMENT MODELS
Three ways to work together.
Pick the model that fits the problem. All three come with the same level of rigour — the difference is scope and duration.
For focused problems
Project
Scoped deliverable with a fixed outcome — a pipeline, a model, a system, a product. Starts with a clear brief and ends with something working.
- Fixed scope and timeline
- Single defined deliverable
- Full handoff with documentation
- Post-delivery support window
For ongoing work
Retainer
Regular engineering capacity on a monthly basis — for teams that have a steady stream of data/AI work and want a consistent technical partner.
- Committed hours each month
- Flexible scope — adapt as priorities shift
- Priority response and availability
- Strategic input beyond just execution
For decisions
Advisory
One or two sessions to work through a specific decision — what to build, whether to buy a tool, how to structure a data team, what’s actually wrong with the current stack.
- 2–3 hour session format
- Written summary of recommendations
- No ongoing commitment required
- Good for pre-build decisions
WHO THIS IS FOR
Right problems. Right teams.
Most of the work comes from teams in telecom, financial services, or technology — not because those are the only verticals, but because those are the domains where the data problems are hardest and the cost of a wrong AI decision is highest.
Gulf-based NRI and enterprise clients get particular attention — the financial planning, data infrastructure, and AI adoption challenges in that region are genuinely interesting and underserved.
Telecom & infrastructure
CDR processing, churn prediction, network analytics, billing intelligence
Financial services & NRI finance
Data platforms, AI-assisted planning tools, regulatory data, Gulf-region NRI needs
Early-stage AI/data products
Founding-stage teams that need a technical co-founder equivalent, not a full agency
Teams with messy data problems
Unreliable pipelines, poor data quality, systems that work in dev and fail in prod
READY TO START?
Tell us what you’re trying to build.
No forms to fill out beyond what you want to share. Just a conversation about the problem and whether there’s a fit.