The questions we hearmost, answered plainly

Brilliqs offers a complete suite of data engineering, analytics and AI services:

Data Infrastructure: Data pipeline architecture and implementation, cloud data warehouse setup (Snowflake, Databricks, BigQuery, Redshift), data lake design, real-time streaming (Kafka, Kinesis) and data orchestration (Airflow, Prefect, Dagster).

Analytics & BI: Dashboard development in Power BI, Tableau, Looker, self-service analytics platforms and embedded analytics integration.

AI & Machine Learning: LLM integration and fine-tuning, ML model development and deployment, vector database setup, chatbot development and predictive analytics.

Data Modernization: Legacy database migration, on-premise to cloud transformation and modern data stack implementation.

Application Development: Data-driven web applications, full-stack custom software and cloud-native development.

We deliver both — full end-to-end platforms and modular components — depending on your needs and current maturity level.

End-to-End Platforms: For organizations starting from minimal infrastructure, we build complete data platforms including data ingestion, cloud warehouse setup, transformation frameworks, analytics dashboards, governance and training. Typically delivered over 3-6 months in phased releases.

Individual Components: When you have existing infrastructure, we focus on specific layers — build just the data pipeline on your current warehouse, create analytics on existing data, or implement AI/ML on your current platform.

Hybrid Approach: Most clients have some existing systems. We assess what's working and what needs building, then deliver a phased solution that integrates with existing tools without requiring complete system shutdown.

Yes. We specialize in both greenfield data platform builds and legacy system modernization.

Greenfield Builds: For organizations with minimal existing infrastructure, we design modern cloud-native data architectures from scratch, select the right technology stack, build scalable pipelines with quality controls from day one and implement governance from the start.

Legacy Modernization: We migrate data from on-premise systems to cloud platforms, rebuild outdated pipelines with modern tools, extract and transform data from legacy databases and refactor monolithic processes into modular architectures, all while managing data migration without downtime or data loss.

Transition Approach: We don't force rip-and-replace strategies. Our methodology includes risk assessment, incremental migration, parallel testing and rollback capabilities to ensure business continuity throughout the transition.

Yes. We take a full-stack approach — we don't just build the backend data infrastructure, we also deliver the user-facing applications and dashboards.

Dashboard & BI Development: Interactive dashboards for operational and strategic decision-making, self-service analytics platforms, real-time monitoring dashboards and mobile-responsive solutions using Power BI, Tableau, Looker and Apache Superset.

Custom Applications: Data-driven web applications, reporting applications, operational dashboards integrated into workflows and cloud-native application development.

Embedded Analytics: Analytics embedded directly into your existing applications, white-label analytics for SaaS products and API-driven analytics integrations.

A beautiful dashboard has no value without reliable data. A perfect pipeline provides limited impact if users can't access the data. We build both, ensuring data flows reliably into applications people actually use.

Brilliqs provides modern AI and chatbot integration services for production environments.

LLM Integration: Integration of OpenAI, Anthropic and Hugging Face models into business applications. Fine-tuning LLMs on proprietary data for domain-specific accuracy. RAG systems that ground AI responses in your data. Production LLM management including cost optimization and performance tuning.

Conversational AI: AI-powered chatbots for customer support, HR and operational queries. Natural language understanding, multi-turn conversation management and integration with existing systems like ticketing platforms and CRM.

ML Operations: MLflow for model versioning and deployment, model monitoring, automated retraining pipelines and A/B testing frameworks.

Vector Databases: Setup and optimization for semantic search and content retrieval.

Common Use Cases: AI customer support agents, intelligent document processing, predictive analytics and enterprise semantic search.

Yes. Tool selection is one of the most critical decisions in a data project. We help in two ways.

Tool Evaluation: Unbiased assessment of your requirements and current tech stack. We evaluate leading BI platforms (Power BI, Tableau, Looker) and cloud data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse, Databricks). We run proof-of-concept implementations to test tools with your actual data and use cases.

What We Consider: Current data volume and growth projections, technical skill levels of your team, budget constraints and licensing preferences, compliance and security requirements (HIPAA, GDPR), integration needs with existing systems and long-term scalability.

We recommend based on your business case, not vendor relationships. Many clients are surprised to learn that the cheaper option costs more over 3 years when you factor in infrastructure, licensing and training.

We offer four engagement models to match different client needs:

Project-Based: Fixed-scope engagements with defined deliverables, timeline and budget. Ideal for specific projects like dashboard development, data migration, or platform build with clear requirements.

Dedicated Pod: A dedicated team of 3-8 engineers working exclusively on your projects. You get a committed team that understands your systems deeply. Ideal for ongoing development needs.

Managed Services: We manage and operate your data platform on an ongoing basis — monitoring, optimization and maintenance. You pay a monthly retainer for continuous support.

Staff Augmentation: We provide individual engineers or specialists who integrate with your existing team. Ideal when you need specific skills (ML engineer, BI developer) for a defined period.

Most clients start with a project-based engagement and expand to a dedicated pod or managed services model as their data maturity grows.

Data engineering projects vary significantly based on data volume, complexity, compliance requirements and technology stack, so we don't publish standard pricing. Every engagement is custom priced.

Our Pricing Approach: We start with a Discovery phase to understand your current data landscape, business objectives and technical constraints. Based on this assessment, we provide a detailed proposal with effort estimation, timeline and cost breakdown.

What Influences Cost: Data volume and sources, number of data pipelines, cloud platform choice, compliance requirements (HIPAA, GDPR), integration complexity with existing systems and whether you need AI/ML components.

Pricing Models: We use fixed price for well-defined scopes, time and materials for evolving requirements and monthly retainers for managed services or dedicated teams.

Get an Estimate: Book a free consultation and we'll provide a tailored proposal within 3-5 business days.

Yes. We regularly run pilots and proof of concepts (PoCs) for clients who want to validate our approach before committing to a larger engagement.

What a PoC Includes: A time-boxed engagement (typically 2-4 weeks) where we build a small-scale version of the solution — one data pipeline, one dashboard, or one AI integration — to demonstrate feasibility, quality and approach.

What You Get: Working prototype, architecture documentation, effort estimation for full-scale implementation and a clear go/no-go recommendation.

Cost: PoCs are priced separately as a fixed-fee engagement. If you move forward with a full project, we credit the PoC cost toward the larger engagement.

When to Request a PoC: When you have a specific data challenge you want to solve, when you're evaluating multiple vendors, or when your internal stakeholders need to see a working example before approving a budget.

Project timelines vary based on scope, but here are typical ranges:

Small Projects (Single Dashboard or Pipeline): 4-8 weeks. Includes discovery, design, build, testing and handover.

Medium Projects (Multi-Pipeline Platform or Full BI Suite): 3-6 months. Includes architecture design, pipeline development, data quality frameworks and analytics layer.

Large Projects (End-to-End Data Platform): 6-12 months. Includes complete data infrastructure, governance, AI/ML components and organizational enablement, delivered in phased releases.

What Affects Timeline: Number of data sources, data quality of existing systems, compliance requirements, stakeholder availability for reviews and whether you need AI/ML components.

Our Methodology: We deliver in 2-week sprints with continuous stakeholder reviews, so you see progress every two weeks and can adjust priorities as needed.

Every Brilliqs engagement follows our proven 4-step methodology:

Step 1 — Discover: We audit your current data landscape, identify gaps, interview stakeholders and align on business objectives. You get a detailed findings report with recommendations.

Step 2 — Design: We architect a solution tailored to your tech stack, scale, compliance requirements and business goals. You get architecture diagrams, tool recommendations and an implementation roadmap.

Step 3 — Build: Our engineers implement the solution iteratively in 2-week sprints with continuous testing and stakeholder reviews. You see working progress every two weeks.

Step 4 — Support: Post-launch monitoring, optimization, knowledge transfer to your team and ongoing maintenance. We don't disappear after go-live.

This methodology has been refined over 20+ years and hundreds of enterprise implementations.

Yes. We offer multiple post-delivery support options to ensure your data platform continues to perform:

Hypercare Period: Free support for 2-4 weeks after go-live. We monitor the system, fix any post-launch issues and ensure smooth adoption by your team.

Managed Services: Monthly retainer-based support including platform monitoring, performance optimization, pipeline maintenance, security patching and SLA-backed response times.

Knowledge Transfer: Comprehensive documentation, training sessions for your internal team and handover of all code, configurations and operational runbooks.

Ongoing Enhancement: We can continue to add new pipelines, dashboards, or AI capabilities as your business needs evolve.

Why It Matters: Data platforms are not set-and-forget systems. They need monitoring, optimization and evolution as data volumes grow and business requirements change. We ensure you're covered.

We follow an agile, sprint-based delivery model designed specifically for data engineering and AI projects:

Sprint Cycles: Every 2 weeks, we deliver working functionality — a data pipeline, a dashboard, or a model — that you can review and test.

Continuous Integration: All code goes through automated testing, code reviews and quality checks before deployment. No shortcuts.

Stakeholder Reviews: Weekly check-ins and sprint demos ensure business stakeholders see progress and can provide feedback before we move forward.

Documentation: Architecture docs, data dictionaries, API specs and operational runbooks are updated continuously, not as an afterthought.

Quality Gates: Data validation, performance benchmarks and security checks at every stage. We treat your data infrastructure as a production system from day one.

Misaligned requirements are the #1 reason data projects fail. We prevent this through structured discovery and communication:

Discovery Workshops: We bring business stakeholders and IT teams together in structured workshops to define use cases, success metrics and data requirements in plain language.

Requirement Documentation: We create clear, visual documentation — user stories, data flow diagrams and acceptance criteria — that both business and technical teams can review and sign off on.

Iterative Validation: Instead of waiting until the end, we validate requirements in every sprint demo. If something doesn't match expectations, we catch it early and adjust.

Dedicated Delivery Lead: Every engagement has a single point of contact who translates business needs into technical tasks and keeps all stakeholders aligned throughout the project.

Outcome: Business teams get what they need, IT teams know exactly what to build and everyone stays on the same page.

We follow enterprise-grade governance practices to keep projects transparent and on track:

Weekly Status Reports: Every Friday, you receive a detailed status report covering sprint progress, completed tasks, blockers, upcoming milestones and risk flags.

Bi-Weekly Sprint Demos: Live demonstrations of working functionality every 2 weeks where stakeholders can see progress, provide feedback and approve next priorities.

Dedicated Communication Channels: Slack, Microsoft Teams, or your preferred platform for day-to-day communication. You're never left waiting for updates.

Project Dashboard: Real-time visibility into task progress, burndown charts and milestone tracking through tools like Jira, Azure DevOps, or your existing project management system.

Escalation Protocol: Clear escalation paths for issues, risks, or delays, with defined response times and resolution owners.

Executive Summaries: Monthly high-level reports for leadership stakeholders who need strategic visibility without technical details.

Change is inevitable in data projects. Here's how we handle it professionally:

Change Request Process: Any new requirement or scope change goes through a formal change request (CR) process. We assess impact on timeline, cost and dependencies, then present you with options.

Prioritization Framework: When new requests come in, we help you prioritize against existing work. You decide what gets added, what gets deferred and what gets replaced.

No Surprises: You always know the impact before approving any change — including effort estimate, cost impact and revised timeline. Nothing gets added without your approval.

Scope Creep Prevention: Clear initial scoping and documented acceptance criteria prevent scope creep. If something was in the original scope, it's included. If it's new, it's a change request.

Flexibility Within Sprints: Within a sprint, we allow reasonable scope adjustments. Across sprints, we maintain discipline to protect timelines and budgets.

Result: You maintain control over your project scope while we maintain delivery discipline.

Go-live is not the end — it's the beginning of the next phase. Here's what happens post-launch:

Hypercare Period (2-4 Weeks): Free intensive support immediately after going live. We monitor the system 24/7, fix any post-launch issues, optimize performance and ensure smooth user adoption.

Knowledge Transfer: Comprehensive training sessions for your internal team, covering system architecture, operational procedures, troubleshooting and maintenance tasks.

Documentation Handover: Complete project documentation including architecture diagrams, data dictionaries, API specs, deployment guides and operational runbooks.

Performance Monitoring: We set up monitoring dashboards, alerts and logging to track system health, data pipeline performance and user adoption metrics.

Ongoing Support Options: After hypercare, you can choose managed services (monthly retainer), ad-hoc support, or a dedicated pod for continued enhancement.

Optimization Phase: We analyze system performance and user feedback, then recommend improvements — from pipeline optimization to new analytics capabilities.

We don't disappear after go-live. We stay until your team is fully confident running the system independently.

Data security is built into every solution we deliver, not added as an afterthought:

Encryption: All data is encrypted at rest (AES-256) and in transit (TLS 1.3). This applies to databases, data lakes, pipelines and API endpoints.

Access Control: Role-based access control (RBAC) with fine-grained permissions. Users get only the access they need — no more, no less.

Authentication: Integration with your existing identity providers (Active Directory, Okta, Azure AD) for single sign-on and centralized user management.

Audit Logging: Comprehensive logging of all data access, queries, modifications and administrative actions. Full audit trails for compliance and forensics.

Network Security: VPC isolation, private endpoints, firewall rules and IP whitelisting to restrict access to authorized networks only.

Secrets Management: Secure storage and rotation of credentials, API keys and connection strings using cloud-native secret managers.

Every data platform we build follows enterprise security standards from day one.

Yes. We regularly work with sensitive and regulated data across multiple compliance frameworks:

Healthcare (HIPAA): We build HIPAA-compliant data platforms with proper access controls, audit logging, encryption and business associate agreements. Our healthcare clients include hospitals, health tech companies and insurance providers.

Financial Services (PCI DSS, SOX): Data solutions that meet banking regulations including transaction audit trails, data residency requirements and payment card data protection.

GDPR & Data Privacy: GDPR-aligned data handling practices including data minimization, purpose limitation, consent management and data subject rights support.

Government Standards: Experience with government-grade security requirements, data sovereignty rules and public sector compliance frameworks.

What This Means for You: We understand the compliance requirements in your industry and build data platforms that meet them — so you can use the platform with confidence, not worry.

Certifications: We operate under ISO 27001 certification and CMMI Level 3 processes, giving you third-party assurance of our security practices.

Yes. Brilliqs operates under enterprise-grade security and compliance standards:

ISO 27001 Certified: Our parent company Synthesys holds ISO 27001 certification for Information Security Management. This covers our entire delivery operation, from development environments to client data handling.

CMMI Level 3: We follow CMMI Level 3 maturity processes for software development and service delivery, ensuring consistent, repeatable and high-quality outcomes.

GDPR-Aligned Practices: We follow GDPR-aligned data handling practices for all clients, including EU-based organizations. This includes data minimization, purpose limitation and support for data subject rights.

SOC 2 Readiness: While we don't hold SOC 2 certification directly, our ISO 27001 controls map closely to SOC 2 requirements. For clients who need SOC 2 compliance, we build data platforms that support your SOC 2 audit requirements.

Cloud Compliance: We work within the compliance frameworks of AWS, Azure and GCP, leveraging their certifications and controls as part of our solutions.

What You Get: Compliance-ready data platforms with documented security controls, audit trails and certification support for your own compliance audits.

Data Ownership: You own 100% of your data, always. We never claim ownership, never sell and never use your data for anything other than delivering your project.

Data Location: Data is stored in your chosen cloud environment. If you use AWS, your data lives in your AWS account. If you use Azure, it's in your Azure subscription. We never store client data on our own infrastructure.

Cloud Provider Choice: You choose the cloud provider and region based on your compliance, performance and cost requirements. We support AWS, Azure, GCP and hybrid environments.

Data Residency: For clients with data residency requirements (EU, India, etc.), we configure data storage in the required geographic regions.

Access During Project: Our engineers access your data only for project work, through your approved credentials and within your security boundaries.

After Project Completion: All credentials are revoked, access is removed and you retain full control of your data platform.

Data Deletion: Upon contract termination, we provide certified data deletion, with documented proof that all access and copies have been removed.

Data governance is not optional — it's foundational. Here's our approach:

Data Governance Framework: We implement governance frameworks that define data ownership, stewardship, policies and standards across your organization. This includes data cataloging, metadata management and policy enforcement.

Data Lineage: We build data lineage tracking so you can trace every data point back to its source, understanding how data flows, transforms and is consumed across your platform. This is critical for compliance, debugging and trust.

Data Quality Management: Automated data quality checks at every pipeline stage, validating completeness, accuracy, consistency and timeliness. Failed data is quarantined, not silently dropped.

Master Data Management: Identifying and managing your critical business entities (customers, products, locations) with deduplication, standardization and golden record management.

Policy Enforcement: Automated enforcement of data policies, including PII detection, masking, access controls and retention rules.

Tools We Use: Great Expectations, dbt tests, Apache Atlas, OpenLineage and cloud-native governance tools (AWS Glue Data Catalog, Azure Purview).

Outcome: Clean, trustworthy data that your teams can rely on for decision-making, not guesswork.

Yes. We routinely work within existing enterprise security environments:

VPN & Private Networks: We connect through your corporate VPN, private network tunnels, or direct peering connections. Our engineers work from within your network perimeter, not from public internet.

VPC & Private Subnets: We deploy and manage data infrastructure within your existing VPC, private subnets and security groups. All data traffic stays within your private network.

Security Policy Alignment: We review your security policies, network architecture and access controls before starting, then design our solution to comply with them.

Firewall & Proxy: We work through your corporate firewalls and proxy servers with proper whitelisting and certificate configuration.

Zero Trust Environments: We support zero trust network architectures with micro-segmentation, identity-based access and encrypted communication between all components.

Compliance Boundaries: For clients with strict data boundaries (e.g., EU data cannot leave EU regions), we configure our entire solution within those boundaries.

Result: Your security team can approve our work with confidence. We integrate into your existing security posture — we don't try to replace or bypass it.

We are cloud-agnostic and work with all major platforms based on your needs:

Cloud Platforms: AWS (Redshift, Glue, Kinesis, S3), Azure (Synapse, Data Factory, Event Hubs), Google Cloud (BigQuery, Dataflow, Pub/Sub).

Data Warehouses: Snowflake, Databricks, BigQuery, Redshift, Azure Synapse Analytics.

Data Orchestration: Apache Airflow, Prefect, Dagster, AWS Step Functions, Azure Data Factory.

Stream Processing: Apache Kafka, Apache Flink, AWS Kinesis, Azure Event Hubs.

Data Transformation: dbt, Apache Spark, PySpark, SQL-based transformations.

BI & Visualization: Power BI, Tableau, Looker, Apache Superset, QuickSight.

AI & ML: OpenAI, Hugging Face, LangChain, MLflow, TensorFlow, PyTorch.

Databases: PostgreSQL, MySQL, MongoDB, Cassandra, Redis.

Our Approach: We recommend the right stack based on your existing investments, team skills, compliance needs and budget — not based on what we're most comfortable with.

Yes. Integration with existing business systems is a core part of what we do:

ERP Systems: SAP, Oracle ERP, Microsoft Dynamics, NetSuite — we build data pipelines that extract, transform and load ERP data into your analytics platform.

CRM Systems: Salesforce, HubSpot, Microsoft Dynamics CRM — we connect CRM data with other sources for unified customer analytics.

MES & Manufacturing Systems: PLC data, SCADA systems, IoT sensors — we integrate real-time production data into monitoring dashboards and predictive models.

HR & Finance Systems: Workday, Oracle HCM, SAP SuccessFactors — we pull HR and financial data for workforce analytics and financial reporting.

Custom APIs: Any system with REST, GraphQL, or SOAP APIs — we build custom connectors and data pipelines.

Integration Approach: We use API-based extraction, database replication, file-based ingestion, or event-driven streaming — whichever approach works best for your systems and data freshness requirements.

Result: Your data platform becomes a unified source of truth that combines data from all your business systems.

We are tool-agnostic. Our priority is solving your business problem, not pushing a specific technology.

Why Tool-Agnostic Matters: Every organization has different needs. A startup needs something different from a Fortune 500. A healthcare company has different compliance needs than a retailer. We match the tool to the use case.

How We Decide: During the Discovery phase, we evaluate your current tech stack, team skills, budget, compliance requirements and long-term goals. Then we recommend the right tools — whether that's Snowflake or BigQuery, Power BI or Tableau, Airflow or Prefect.

What We Avoid: Vendor lock-in. We don't recommend tools just because we have a partnership or certification. We recommend based on what gives you the best outcome.

Reference Architecture: While we're tool-agnostic, we do follow proven reference architecture patterns — layered data architecture, separation of compute and storage, infrastructure as code and modular pipeline design. These patterns work regardless of which specific tools you choose.

Yes. Tool selection is often one of the most critical decisions in a data project — and one where many organizations make costly mistakes.

What We Provide:

• Unbiased assessment of your requirements and current tech stack
• Evaluation of leading BI platforms (Power BI, Tableau, Looker) and cloud data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse, Databricks)
• Proof-of-concept implementations to test tools with your actual data and use cases
• Total cost of ownership analysis including licensing, infrastructure, training and maintenance

What We Evaluate: Current data volume and growth projections, technical skill levels of your team, budget constraints and licensing preferences, compliance and security requirements (HIPAA, GDPR), integration needs with existing systems and long-term scalability.

Our Recommendation: Based on your business case, not vendor relationships. Many clients are surprised to learn that the cheaper option costs more over 3 years when you factor in infrastructure, licensing and training.

Common Question: Should we go with Power BI since we already have Microsoft 365? Sometimes yes, sometimes no. We evaluate based on your actual analytics needs, not just what you already own.

Performance, scalability and reliability are engineered into every data platform we build:

Performance Optimization: We use partitioning, indexing, columnar storage, query optimization and caching strategies to ensure fast query response times, even on billion-row datasets.

Scalability by Design: We build on cloud-native architectures that scale horizontally, adding compute or storage as your data grows without performance degradation. Separation of compute and storage (Snowflake, BigQuery) means you can scale each independently.

Reliability Engineering: Automated retries, dead-letter queues, circuit breakers and fallback mechanisms ensure data pipelines recover from failures automatically. No manual intervention needed.

Monitoring & Alerting: Real-time monitoring of pipeline health, data freshness, query performance and system resource usage. Alerts trigger before problems impact users.

Load Testing: We stress-test data pipelines with production-scale data volumes before go-live, identifying bottlenecks and fixing them proactively.

Cost Optimization: We monitor cloud resource usage and optimize for cost without sacrificing performance — right-sizing compute, using spot instances where appropriate and archiving cold data.

Result: A data platform that performs fast, scales with your business and stays reliable under pressure.

Yes. While we specialize in cloud data platforms, we regularly work with on-premise and hybrid environments:

On-Premise Deployments: We design and build data platforms that run entirely within your data center, using on-premise databases, servers and networking. This is common for government, healthcare and financial clients with strict data residency requirements.

Hybrid Environments: Most common scenario — some data stays on-premise (for compliance or latency reasons) while analytics and AI run in the cloud. We build secure, performant connections between on-premise and cloud systems.

Cloud Migration Path: We help clients migrate from on-premise to cloud incrementally, without disruption. We can run both environments in parallel during the transition.

Integration Tools: We use tools like AWS Direct Connect, Azure ExpressRoute, Google Cloud Interconnect and secure VPN tunnels to connect on-premise systems with cloud platforms.

Security: Hybrid architectures require careful security design. We implement encrypted tunnels, network segmentation and zero-trust access controls to secure data moving between on-premise and cloud.

Our Recommendation: We assess your specific situation and recommend the right architecture — whether that's full cloud, hybrid, or staying on-premise. We don't push clouds for the sake of clouds.