Governance reviews, privacy law, and cold starts don’t have to stall delivery.
Intellias generates data that behaves like production data statistically, relationally, and spatially, so your teams can build, test, and demo now, and switch to real sources later without rebuilding anything.
Synthetic data solutions matched to the problem
The generation method follows what the data needs to preserve: structure, distributions, relationships, behavior, or specific scenarios.
Create production-like datasets that mirror the target schema and relationships before live data is available.
- Preserve entities, data types, keys, relationships, business rules
- Build and validate pipelines, analytics layers, integrations, dashboards
- Support repeatable development, regression testing
Generate tabular and time-series data that retains the statistical patterns required for analytics and testing.
- Model distributions, correlations, ranges, multi-variable relationships
- Create representative populations, synthetic histories
- Apply fidelity-based statistical/ deep generative methods
Tools: NumPy, SciPy, copulas, SDV, CTGAN, TVAE
Model how entities interact so patterns emerge from behavior, proximity, and density as opposed to random records.
- Simulate congestion, churn clusters, adoption, neighbor effects
- Preserve geographic and hierarchical structures
- Support network, infrastructure, capacity, regional planning scenarios
Tools: Mesa, MultiGrid, NetworkX
Generate realistic-looking identities and transactional records without copying real customer identities into non-production environments.
- Create names, addresses, phone numbers, IDs, histories
- Apply locale-aware formats for realistic application/workflow testing
- Populate development, QA, and demo environments
Tools: Faker, Mimesis, custom locale generators
Generate unstructured data for systems that depend on realistic text rather than structured records alone.
- Create synthetic notes, tickets, contracts, conversations, etc
- Control output through schemas, personas, templates, scenarios
- Support document processing, NLP, conversational systems, workflow testing
Tools: Claude, Azure OpenAI, LangChain
Build specific conditions into synthetic datasets instead of waiting for rare examples to appear naturally.
- Inject volume spikes, fraud scenarios, churn waves, rare conditions, recovery periods
- Keep events consistent with the surrounding dataset/business rules
- Reproduce the same scenarios across demos, QA, regression testing
Tools: SDV, Tonic.ai, Gretel.ai, Locust-like scenario injectors
How we deliver the practical results
Synthetic data generation is run as a four-phase engagement with validation built into every phase.
Data discovery and information architecture
(Weeks 1–2)
Profile schemas, entities, relationships, distributions, and business rules. Map the target data model and analytics structure the synthetic data must support.
Generation design and specification
(Week 3)
Choose the required fidelity and generation methods. Define constraints, random seeds, and volume targets to keep output fit for purpose and reproducible.
Synthetic data generation
(Weeks 4–6)
Generate scalable, relationally consistent datasets with reproducible runs. Inject relevant variation and edge cases, such as fraud spikes, churn waves, or rare conditions.
Validation and handover
(Weeks 7–8)
Validate statistical fit, schema, referential integrity, and privacy or re-identification risk before handover.
Why Intellias for synthetic data generation
We view synthetic data as a multidisciplinary engineering discipline, spanning data engineering, modeling, application logic, privacy, and downstream systems.
We choose the technique based on the signal you need: statistical synthesis, agent simulation, procedural generation, deep models, or LLMs, each suited to different data contracts.
Generators are designed against your actual architecture: schemas, relationships, pipelines, dashboards, and business rules.
We test both dimensions, data usability and safety, following NIST evaluation guidelines rather than assuming one guarantees the other.
Rare events are seeded, reproduced, and tested instead of being left to chance in a production sample.
You get seeded generators, validation suites, and configuration, so datasets can be regenerated and extended as needs evolve.
FAQs
Intellias delivers synthetic data as a service through a four-phase engagement covering data discovery, generation design, synthetic data generation, and validation and handover. Depending on the use case, the deliverables can include validated datasets, seeded generators, validation suites, and configuration that your team can reuse and extend.
Masking starts with real records and tries to strip or obscure identifying fields; the underlying real data and its re-identification risk are still there. Synthetic data generation never starts with real individual records in the first place — names, IDs, and payment histories are generated procedurally, or an entire dataset is sampled from a fitted distribution or trained model. There’s no original record to leak.
It can, if the synthetic data is built to the target schema, relationships, and distributions from the start. The closer those characteristics match the production data contract, the lower the risk of rework when real data is introduced.
Synthetic data generation for AI can provide structured or unstructured datasets for training, testing, and AI-dependent workflows. Intellias can use statistical or deep generative methods for structured data and LLM-based generation for notes, tickets, contracts, conversations, and other text, with schemas, personas, templates, and scenarios controlling the output.
Yes. Deployment can run in the cloud or on-premises, and LLM-based generation can use self-hosted models where data can’t leave your environment — a common requirement in healthcare and financial services engagements.
Every engagement with Intellias includes statistical parity checks against reference distributions, schema and referential-integrity validation, and a privacy or re-identification risk assessment before data is handed over. On our identity-platform engagement, that discipline runs as ten automated checks, including a PII sweep across nested data structures, gating every write.
Yes, deliberately. Fraud spikes, churn waves, and rare clinical conditions can be seeded into a generation run at a specified magnitude and point in the timeline, which is often the only practical way to exercise dashboards and alerting logic against signals that real samples rarely contain in sufficient volume.
Most engagements start with one target system or dataset and a defined fidelity level, run the four-phase process over roughly eight weeks, and produce a validated dataset plus a specification your team can extend to additional systems on its own.
Look at whether the company selects generation methods around the required data characteristics, designs against your actual schemas and business rules, and validates utility and privacy separately. Reproducible edge cases, validation suites, and reusable generators also matter when synthetic data needs to support more than a one-time demonstration.