Enterprise AI · Now in design partner phase

The system of context.

The system that manages context for individuals, teams, and the enterprise — stitched into one governed plane. Together, that's all the context AI will ever need.

Governed execution.  //  Verified outcomes.  //  Zero migration.

ShareContext · live system
PEOPLE teams · roles · intent DATA warehouse · KG · streams SYSTEMS & TOOLS MCP · skills · APIs AI PLATFORMS stateless · LLMs & Custom AI Models SHARECONTEXT System of Context CONTEXT · EXECUTION · MEMORY DECISIONS scored · tracked ACTIONS governed · auditable OUTCOMES measured · learned
ANCHOR STRATEGY DETERMINISTIC EXECUTION MEASURE DECISION IMPACT COMPOUNDING INTELLIGENCE DECISION LIFECYCLE closed loop
COMPOUNDS DAY 1 SPARSE GRAPH TWO MONTHS LATER 10× DENSER ≈ 47 nodes · 89 edges 477 nodes · 1,927 edges
Estimate drift View detailed
— THE PROBLEM Compounding Orchestration drift. Errors compound across a 20–50 LLM chain. A 1% per-step slip becomes a 40% wrong answer. 100% 0% 0 25 CALLS IN CHAIN → 60.5% · 1% 7.7% · 5% 0.5% · 10% — THE ANSWER Compounding Orchestration intelligence. ShareContext learns from every chain — sharper, cheaper, more accurate over time. DETERMINISTIC RULES cheap · fast · exact LLM CALLS flexible · noisy FOUR LEVERS THE ENGINE BALANCES COST LATENCY ACCURACY RISK
The Problem

Capability isn't the limit. Coordination is.

Enterprises don't have a knowledge problem. They have a context routing problem. The smartest model in the world can't act on what it doesn't see — and today, almost none of an enterprise's operational context is system-owned.

01
The bottleneck is coordination.
Capability isn't the limit anymore. Re-routing context across people, tools, and workflows is. Every project re-explains what should already be known.
02
Judgment never compounds.
Every decision starts from scratch. The senior analyst's methodology, the operator's intuition, the playbook — all of it leaves when people leave.
03
Intent isn't system-owned.
Business logic lives in individuals. Not shared, not executable, not governed. It's in heads, decks, Slack threads — re-explained on every request, every time.
In one line

You have a system of record.
You're missing a system of context.

The Category

A new system, alongside the system of record.

For thirty years, enterprise software has captured what happened — Salesforce, Workday, ERP. None of them govern what happens next. That's the gap.

Today · Established

The system of record

Captures what happened. Stores it. Reports on it. CRMs, ERPs, data warehouses — billions in revenue, decades of incumbent. Indispensable, and not enough.
Past tense
New · ShareContext

The system of context

Governs what happens next. The compounding intelligence layer that captures intent, executes deterministically, measures the real outcome, and learns. Decisions that compound.
Forward tense
The system of record captures what happened. The system of context governs what happens next.
The Platform

Three components. One loop.

ShareContext is built on three primitives. Each is useful alone. Together, they compound into something nothing else in the market provides — a system that gets smarter with every decision it touches.

— 01 / Context

Context

Automatic · Personalized · Governed

Personal, team, and enterprise context fuse into every prompt. The model sees the schema, the bridge tables, the roles, the playbooks. No more re-explaining.

  • Allowed-schema enforced at the planner
  • Knowledge graph as first-class citizen
  • Personal + team + enterprise fusion
  • Zero migration · works with your data
— 02 / Execution

Execution

AI-driven · Skills-guided · Deterministic

Frontier models decide what to do. Our governed execution plane decides whether and how. The planner is physically incapable of referencing entities outside the allowed-schema whitelist. Every generated query is statically validated before it touches data. Skills are declarative.

  • Skills as YAML — no engine release
  • SQL validated before warehouse touch
  • Self-healing on dialect or schema error
  • Confirms intent on ambiguous actions
— 03 / Memory

Memory

Compounding · Auditable · Institutional

Every successful analysis becomes pattern. Every decision carries its evidence. The methodology library grows with each query — institutional IP that survives turnover. Then the Asset Factory turns those patterns into materialised, governed tables.

  • Methodology auto-learn from successful runs
  • Asset Factory · recipes become production tables
  • Decision Quality Score updates daily
  • Replayable trace · SOX/SOC2-ready evidence
Decision Intelligence Lifecycle

A self-reinforcing engine for predictable outcomes.

Four stages. One closed loop. Methodology, evidence, and outcomes deposit back into context — the system gets smarter and cheaper the longer it runs.

SHARECONTEXT system of context
Anchor Strategy
Deterministic Execution
Measure Decision Impact
Compounding Intelligence
01.
Anchor Strategy

Every cycle begins with intent. Goals, constraints, and the business question are captured as structured context — not lost in a chat.

02.
Deterministic Execution

The model plans against the allowed schema. Every query is validated before it runs. Skills execute deterministically. Every step is inspectable evidence.

03.
Measure Decision Impact

Real-world outcomes are tracked against the original intent. The Decision Quality Score updates daily as ground-truth data lands.

04.
Compounding Intelligence

The methodology, the evidence, the outcome — all return to the system of context. The next decision starts ahead of where the last one started.

For Enterprise

Built for how enterprises actually work.

Frontier AI is the easy part. Making it survive contact with a real enterprise — data sprawl, governance, turnover, regulators — is the hard part. ShareContext is engineered for the hard part.

AI Platform for Enterprise

Works with your data, knowledge, skills, and governance. Zero migration. No vendor lock-in. Model-agnostic.

100% Governed Execution

The AI asks before acting in ambiguous situations. Every insight backed by evidence. Fully auditable, end to end.

Minimal Friction, Fast Time-to-Value

Container deployment in your VPC. Automated context mapping. No specialised teams required. Value realised week one.

Reduces Human Dependency

Institutional knowledge compounds in the system, not in irreplaceable analysts. Less reliance on key people.

Proactive Intelligence

Surfaces insights you haven't asked for. Uncovers blind spots before they become crises. The system tells you what to look at, not just what you asked.

AI without risk, complexity, or loss of governance.

The Comparison

Context is the missing ingredient. We're the system for it.

Personal · team · enterprise. We manage all three. Together, that's the context AI needs to act.

Category One

The best BI tool

Looker, ThoughtSpot, Mode ship dashboards built on stale assumptions about what to ask. We answer the question that was actually just asked — grounded in the live schema. The dashboard becomes a downstream artifact, not the destination.

vs. Looker · ThoughtSpot · Mode
→ Live questions, not stale dashboards.
Category Two

The best semantic layer

Cube and dbt Semantic Layer define metrics in YAML and stop there. We define them, generate them in plain English, govern them via allowed-schema, and let every analysis that touches them teach the system more.

vs. Cube · dbt Semantic Layer
→ Living metrics, not YAML files.
Category Three

The best agent framework

LangChain and AutoGen are libraries that hand you the agent and leave context, governance, and audit as your problem. We ship the closed loop with the allowed-schema guarantee — the part nobody else has solved.

vs. LangChain · AutoGen
→ Governed agents, not libraries.

Context is the missing ingredient. We're the system for it. They aren't.

Built for the enterprise
Deploys in your VPC Zero migration Model-agnostic Your data, your control
Why Now

The window is open.

Three forces have converged. The category exists now because the conditions exist now.

i.

LLMs crossed the threshold.

Frontier models can now generate production-grade SQL — but only against schema they can see. The bottleneck moved from model quality to context quality.

ii.

Governance is mandatory.

Every CISO at every Fortune 500 has an "AI on data" project blocked on auditability. SOX, SOC2, HIPAA, EU AI Act — the demand is regulatory, not preference.

iii.

Warehouse sprawl peaked.

Mid-market enterprises run BigQuery + Snowflake + ClickHouse + DuckDB simultaneously. The engine-agnostic layer is finally a category, not a feature.

Help

What is ShareContext? Ask.

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Appendix

Investor Q&A.

Questions that come up. Click to expand each.

Q01 Won't Microsoft, Google, or OpenAI just build this?
The Ecosystem Trap: Hyperscalers require 100% data lock-in. Enterprises are deeply fragmented. ShareContext is agnostic and sits on top of their existing, messy infrastructure.
Built on Hyperscaler Gaps: Engineered to solve orchestration and governance blind spots identified after extensive research into AWS, Azure, and GCP intelligence stacks.
Intelligence vs. Execution: OpenAI and Anthropic build horizontal intelligence. We leverage their models but own the enterprise execution and governance control plane.
Q02 Enterprise sales take 9–12 months. How do you survive on a Seed?
Trusted Advisor Pipeline: We bypass cold IT outbound. Our pipeline consists of enterprise leaders advised for two decades, serving as design partners.
Co-Created Product: ShareContext was built directly to solve the active bottlenecks these leaders are facing today.
Immediate Traction: Verbal commitments for paid pilots (convertible to LOIs), a live pilot with an NGO, and active channel partner discussions.
Q03 “Zero Data Migrations” sounds like marketing. How does it actually work?
Pluggable Collectors: We query existing databases (SQL) and apps (CRMs) securely in place. No new data lake required.
Context, Not Storage: We map where data lives and how it relates via our semantic Context Graph, without duplicating raw data.
Cost Efficiency: Separating AI reasoning instructions from raw compute drastically optimizes cloud and API costs.
Q04 How do you guarantee 100% governed execution against AI hallucinations?
The Evidence Engine: LLMs cannot generate facts. They use a strict Plan → Assess → Act loop on append-only, immutable enterprise evidence.
Human-in-the-Loop: Automatically pauses for human approval in ambiguous scenarios.
Audit Ledger: Every query and action is mathematically traced and logged. Compliance teams get the exact “why” behind every AI decision.
Q05 Is this a niche vertical SaaS product, or a horizontal platform?
Horizontal Infrastructure: Most industries have highly fragmented data silos.
Skill Portability: The core architecture (querying databases, executing APIs) applies universally to Supply Chain, Finance, and Retail.
Marketplace Play: As we codify business skills, we transition from software to a marketplace of executable enterprise capabilities.
Q06 How does “units of context” pricing work?
The Death of Seat Pricing: AI reduces seats, and usage-based API pricing punishes adoption.
Units of Context: We charge based on the scale of data sources and skills mapped into the Context Graph.
Value Alignment: As clients connect more systems, AI capabilities scale — and they move into a higher pricing tier, aligned with structural lock-in.
Q07 By connecting to everything, don't you become a massive security vulnerability?
No Raw Data Ingestion: Data never leaves the client's environment. We do not train models on their proprietary data.
Metadata Routing: The Context Graph stores schemas and logic. The engine queries data temporarily for execution, then immediately drops it.
Inherited Permissions: The platform strictly enforces existing enterprise role-based access controls (RBAC).
Q08 Why wouldn't internal engineering teams build this with open-source tools like LangChain?
The Vision Gap: Internal teams are busy keeping the lights on. Architecting a horizontal execution layer requires a cross-enterprise perspective built over decades.
Tools vs. Platforms: LangChain is raw material. Building an enterprise execution engine from it is like building Salesforce from SQL.
Maintenance Nightmare: Custom orchestration breaks as models shift weekly. We offer a turnkey, model-agnostic platform.
Governance Out-of-the-Box: Open-source lacks our proprietary Memory Ledger and native auditability required by compliance teams.
Q09 Why is this the right team to pull this off?
In-the-Trenches Experience: This architecture is the direct result of 25 years spent architecting and leading massive data and AI platform rollouts for global enterprises.
Understanding the Buyer: We know exactly why enterprise IT teams reject new tools, how compliance evaluates risk, and how RevOps measures ROI.
Q10 Why is ShareContext uniquely positioned right now?
The LLM Reasoning Shift: Models have just crossed the threshold from text generators to reasoning and planning engines capable of executing workflows.
The Orchestration Vacuum: While models evolved, enterprise infrastructure to control them has not.
Window of Opportunity: Hyperscalers are distracted by model wars and ecosystem lock-in. The window to establish an agnostic execution layer is open right now.
Q11 What exactly does this $3M Seed round buy?
Platform Hardening: Advancing the Evidence Engine and Context Graph to a fully hardened, compliant enterprise platform.
Go-To-Market Execution: Converting initial design partners to paid contracts and onboarding channel partners.
Series A Milestones: Achieving $1M+ in ARR, 5+ deployed enterprise contracts, and proven zero-migration onboarding in under 30 days.
Q12 Is the ultimate goal acquisition, or a standalone company?
The Operating System for AI: ShareContext is built as a standalone, horizontal platform designed to be the OS for enterprise AI execution.
The Ultimate Data Moat: The ShareContext Ledger codifies Fortune 500 business logic, becoming an irreplaceable, compounding enterprise asset.
Strategic Position: Architecture and pricing support an independent IPO path, though hyperscaler acquisition remains a natural exit.
Q13 What measurable benefits should we expect — and how quickly?
50–80% LLM cost reduction on repeat-shape questions once methodology is cached. The system gets cheaper the longer it runs.
2–4 weeks → minutes on new KPI ship time. Plain-English to production metric, allowed-schema enforced — the BI ticket queue collapses.
$500K–$2M warehouse migration avoided. Multi-engine, no vendor lock-in. BigQuery + DuckDB + ClickHouse + MCP — your warehouse stays where it is.
15–40% analyst time recovered from the SQL rework loop. Self-healing recipe book means failed queries become reusable corrections, not analyst rework.
Q14 Why is agentic orchestration so hard — and what does ShareContext do about it?
Compounding drift is the default failure mode. LLMs are non-deterministic. A real analysis chains 20–50 of them — plan, generate SQL, validate, execute, summarise. A "tiny" 1% per-step slip becomes a 40% wrong answer by the end. Adding QA agents reduces error but spikes cost and latency.
Orchestration is a 4-way balance. Cost, latency, accuracy, and risk all pull against each other. You can't optimise all four at once — and getting it wrong silently is the most expensive outcome in production.
ShareContext orchestrates the balance per step. Deterministic rules where they suffice (cheap, fast, exact). LLM calls where judgment is needed (under guardrails). The engine learns from every run — so the orchestration gets sharper, cheaper, and more accurate over time, like the best of AI.
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Built for the decisions that matter.

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