One container. Your VPC. Your data. Your governance. ShareContext sits alongside the warehouses, databases, and identity providers you already have — and starts producing value before the kickoff meeting ends.
Everything ShareContext needs to plan, reason, govern, and remember runs inside one container deployed to your cloud, your VPC, or your Kubernetes cluster. It talks out to the warehouses, databases, and LLM gateways you already use. Nothing talks back.
Frontier LLMs decide what to do. The execution plane decides whether — and how. Capabilities are declarative, auditable, and improve every time the system runs.
The planner is physically incapable of referencing entities outside the allowed-schema whitelist. Every generated query is statically validated before it touches data. Plans are inspectable; users approve at the right gates.
Each skill is a deterministic, governance-approved agent behaviour written in declarative YAML — vetted SQL patterns, segmentation logic, methodology recipes. Skills are versioned, diff-able, and promoted via a Draft → Approved workflow.
Metrics are not someone's BI dashboard. They're proposals from AI and analysts, vetted by governance, and committed to a shared definition. Metric Studio captures formula, owner, refresh cadence, and the trend points behind each value.
Point a scanner at a warehouse and it auto-extracts the schema, samples shapes, infers entity types, and proposes a starting Knowledge Graph. Reviewers approve nodes/edges in minutes — the graph compounds with every analysis after that.
No multi-month integration. The container is the integration. Run it, point it at a warehouse, and watch the engine produce value the same day.
docker run (or a single Helm chart) on your cloud or on-prem cluster. The container brings its own Postgres, Fuseki, OpenSearch, and nginx — or wires up to your existing ones via env vars.No vendor lock-in. ShareContext is model-agnostic — Gemini, Claude, GPT, open-weights, on-prem SLMs. The engine auto-selects per step, learns from outcomes, and respects whatever policy you set on top.
Pin a model per step, set per-user or per-org budgets, route by data sensitivity. Sensitive workloads can stay on a private endpoint while everything else goes to the cheapest competent model. The engine learns the trade-off automatically from observed accuracy + cost.
Per-user API keys are supported for cost attribution. Or use a single corporate gateway — the call surface is identical.
ShareContext is built on the Model Context Protocol — every tool, connector, and skill is a first-class MCP. Add your own. Scale them independently. Govern them at the policy layer.
Internal APIs, custom data sources, line-of-business tools, third-party services — anything that speaks MCP is a first-class peer to the built-in capabilities. No engine release required.
Heavy connectors run as separate processes/containers. The engine talks JSON-RPC over the MCP gateway, so any single MCP can be sharded, replicated, or moved to a private region without touching the rest of the stack.
Nothing crosses the perimeter that you didn't approve. The container runs inside your VPC. Warehouse traffic stays internal. LLM traffic goes through your gateway (Bedrock, Vertex, Azure OpenAI, or a self-hosted model) under your contract and audit log.
The Decision Ledger, Knowledge Graph, methodology, and skills are your data — stored in Postgres + Fuseki inside the container. Yours to keep, to export, to migrate.
Copilots draft text and reach for tools. ShareContext is a control plane. It plans against an allowed-schema whitelist, executes through deterministic skills, gates at user/policy/governance checkpoints, records evidence for every step, and learns from outcomes.
Copilots help one person. ShareContext makes the organization's reasoning repeatable and replayable.
The container starts at 4 vCPU / 16 GB and scales horizontally. Heavy MCPs run as separate processes — replicated, sharded, or moved to dedicated nodes without redeploying the engine. The Knowledge Graph and Decision Ledger sit on the same Postgres/Fuseki you'd scale for any read-heavy workload.
For multi-region or multi-tenant deployments, the same container is the building block — wire them together with your service mesh.
The data is yours. Skills are declarative YAML. Methodology lives in your KG. The Decision Ledger is portable Postgres. The Knowledge Graph is standard RDF/SPARQL via Apache Jena. None of it is a proprietary blob.
Model-side: the engine speaks every major frontier model and is model-agnostic by design. Migrate between Gemini, Claude, GPT, or a private model without touching skills.
No. ShareContext reads your warehouses where they are. The context scanner indexes metadata into the KG (table names, columns, joins, cardinalities) — not the rows themselves. Row-level data is fetched on demand, with an audit trail, and stays in your warehouse.
Each workflow step (Plan, SQL gen, Validate, Synthesize, Disambig) has a default model class — strong-reasoner vs. fast-generalist vs. local SLM. The engine learns from accuracy + cost telemetry which model is winning for which step, and shifts traffic accordingly.
Operators can override at any granularity: per step, per skill, per user, per data sensitivity tag. Policy beats learning.
One container image. Pull it into ECR/GCR/ACR. Run it on Cloud Run, Fargate, GKE, EKS, OpenShift, or a single VM — anywhere a container runs.
Two env vars are mandatory: your IdP issuer and a warehouse connection string. Everything else is optional. Postgres is bundled; swap it for your own with a single env var.
Three layers. Schema: the planner cannot reference entities outside the allowed-schema whitelist. Skills: only Approved skills run in production; Draft skills are sandboxed. Policy: pause-and-ask gates fire at user-defined sensitivity boundaries.
Every step, prompt, query, and intermediate result is logged. Compliance teams replay any decision from the ledger.
The Knowledge Graph + Methodology library compound per-customer. The system learns the customer's data, their conventions, their decision patterns — and that learning becomes their asset, not ours. Frontier models change weekly; the methodology that knows your business doesn't.
Models commoditise. Context doesn't.
Because the container runs in your VPC, it inherits the controls of your cloud — network policy, KMS, IAM, audit log, encryption-at-rest. SOC2 / ISO27001 evidence for the deployment is whatever your platform team already produces. ShareContext adds an in-app audit ledger (every prompt, every step, every approval), exportable to your SIEM.
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