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Impart A.I.

STACK // VENDOR-AGNOSTIC BY DESIGN

What we build on.

Five layers, chosen per engagement. Selection is driven by latency, residency, regulatory profile and total cost of ownership, not by which vendor ran the most polished demo. The operating system around the model is more durable than the model itself, so the model layer stays swappable.

Layer 01

Models

Vendor-agnostic at the model layer. Selection is a per-engagement decision driven by latency, residency, cost per token, and the regulatory profile of the use case.

  • 01Anthropic ClaudeLong-context reasoning, agent orchestration
  • 02OpenAI GPT-4 familyGeneral-purpose, function-calling, vision
  • 03Google GeminiMultimodal, document understanding
  • 04Azure OpenAIEnterprise residency, SA/EU deployments
  • 05AWS BedrockModel marketplace, residency, governance
  • 06Local open-weight modelsAir-gapped or sovereignty-bound deployments

Layer 02

Data foundation

Where most AI engagements actually fail. The data layer is architected first, with POPIA constraints baked in at the schema level rather than added on later.

  • 01PostgreSQL + pgvectorPrimary store with vector retrieval
  • 02Snowflake / BigQueryAnalytics warehouse and feature store
  • 03Pinecone / WeaviateSpecialist vector workloads at scale
  • 04SupabasePOPIA-friendly application database
  • 05S3-compatible object storesDocument and asset retention

Layer 03

Orchestration

How the agents move. Tool use, retries, fallbacks, queues. Boring infrastructure, kept that way deliberately.

  • 01LangGraphStateful agent graphs and multi-step workflows
  • 02TemporalDurable workflow orchestration
  • 03n8n / ZapierLightweight integrations to existing systems
  • 04Make.comOperations-team-friendly automation

Layer 04

Observability

Every system Impart ships into production has telemetry from day one. You see the same dashboards we do.

  • 01LangfuseTrace-level observability for LLM workflows
  • 02Helicone / OpenTelemetryToken spend, latency, error rates
  • 03SentryApplication errors and regression alerts
  • 04Custom KPI dashboardsTied to the cost line the system replaces

Layer 05

Front of house

What the end user actually sees. Whether it is a chat surface, an embedded widget, or a back-office dashboard, the front layer is built for the operator running the function, not for the demo.

  • 01Next.js + ReactCustomer-facing AI surfaces
  • 02Tailwind CSSDesign system delivery
  • 03Vercel / CloudflareEdge-deployed application hosting
  • 04Custom dashboardsOperator-grade tooling for the functions we automate

Want stack recommendations against your specific use case?

The Audit phase is where vendor selection happens. Walk us through the function, we walk back with a stack.

Request an audit