Data not ready for agents
Pipelines exist for analytics, not for real-time agent context, freshness SLAs, or permission-aware retrieval.
Platform
Turn raw, fragmented data into trusted AI context
The 2026 bottleneck is not the model. It is data readiness. DynasAI is the front layer that collects, evaluates, and processes multi-source data — then feeds governed agents while compute stays on the cloud you choose.
The market gap in 2026
Enterprises are funding agents faster than they are preparing the data those agents need. Independent 2026 research points to the same hole: plumbing, context, and governance — not GPUs.
- of enterprises lack a data foundation to run agentic AI securely at scale
- 85%
- have the data foundation required for production agents
- 15%
- of architects delayed or cancelled AI projects over data, governance, or compliance
- 95%
- of enterprise data is sufficiently described and contextualized for agents
- ≤20%
Fivetran / Redpoint 2026 Agentic AI Readiness Index
Fivetran / Redpoint 2026
Cloudera enterprise survey, via CIO
Teradata 2026 Agentic AI report
Why pilots stall — and what comes next
Today: 60% of enterprises invest millions in agentic AI while 41% still run agents on unreliable, poorly governed data. Silent failures look correct until stale, siloed, or mis-permissioned context compounds. RAG demos hide this — production corpora, ambiguous queries, and retrieval noise drop decision-ready accuracy sharply. Only about 12% of enterprises have centralized control over the agents they already run.
Through 2026–2027: EU AI Act enforcement, GDPR incident reporting, and NIS2 raise the bar for lineage, access control, and audit evidence. Hybrid and private deployments are rising as teams reclaim sovereignty — 66% moved AI workloads back from public cloud in the last year. Buyers now rank permissions and ingestion equally, and they refuse to lock the context layer to a single model vendor. The winning stack is a governed context layer on the customer’s AWS, Azure, or GCP — not another demo chatbot.
Where the market is underserved
Vendors sell pipelines, lakes, or models. Few own the front layer that makes multi-source data easy to evaluate, process, and automate — with the customer still in control.
Context fragmentation
Silos across SaaS, warehouses, files, and streams. Agents cannot reason across functions without unified lineage and meaning.
Evaluation after the fact
Teams ship RAG without retrieval quality, faithfulness, or conflict-handling checks — then discover silent failure in production.
Cloud lock-in vs sovereignty
Provider-native retrieval is convenient, but most enterprises will not consolidate context onto one vendor. They need BYO cloud and residency.
Compliance as paperwork
GDPR, US privacy, and the EU AI Act need technical controls — DPIA evidence, audit logs, and data minimization — not slide decks.
Cost without architecture
Hyperscaler spend grows while data quality stays unmeasured. Teams need cost-aware processing on the cloud they already pay for.
How DynasAI processes data
One front layer. Your backend. Collect from anywhere, evaluate before use, process with versioned transforms, automate with audit trails.
Collect from any source
APIs, warehouses, SaaS exports, documents, webhooks, and batch files — unified ingestion without moving ownership off your cloud.
Evaluate before agents touch it
Score completeness, freshness, PII risk, retrieval recall, and groundedness. Gate indexes the way CI gates code.
Process and contextualize
Normalize, enrich, chunk, and attach lineage so agents receive meaning — not raw dumps. Hybrid retrieval and reranking ready for production RAG.
Govern access at retrieval time
RBAC/ABAC follows the data. Agents inherit permissions; queries, sources, and decisions are logged for EU and US audits.
Run on the cloud you choose
AWS, Azure, or GCP — managed templates or your VPC. We recommend the cost-effective mix; you keep data control and residency.
Automate with human gates
Feed governed workflows. Pause for review on conflicts, knowledge gaps, or policy hits — then promote with eval evidence.
Built for today’s rules and tomorrow’s scale
GDPR & EU AI Act
Minimization, purpose limits, residency options, and evidence packs that support incident reporting — not separate binders per framework.
Learn more →US enterprise standards
Encryption, access logs, SOC-aligned controls, and customer-owned tenants for regulated US workloads.
Multi-cloud backends
Bedrock, Vertex AI, Azure OpenAI, BigQuery, S3, Fabric — DynasAI orchestrates; your hyperscaler runs the work.
Learn more →Future-ready context layer
Independent of any single model provider. Hybrid retrieval, eval suites, and incremental re-index as agents become multi-step operators.
Data-readiness sprint
A 2–4 week engagement that maps sources, scores quality, picks AWS / Azure / GCP, and leaves a governed pipeline — before you scale agents.
Source inventory
Catalog warehouses, APIs, SaaS, files, and streams. Mark ownership, residency, and PII.
Quality & retrieval eval
Freshness, completeness, permission gaps, recall@k, and groundedness on a golden question set.
Cloud & cost recommendation
Stay in your VPC or use a managed template. Right-size Bedrock, Vertex, or Azure OpenAI.
Governed pipeline
Versioned transforms, lineage, access at retrieval time, and an eval gate before production indexes.
Close the 85/15 data gap before you scale agents
Start with a data-readiness sprint: source map, quality eval, cloud recommendation, and a governed pipeline on AWS, Azure, or GCP.
