Context-stitching platform

Bring what you have.
Leave with something complete.

Drop in sources, search a question, or talk through a problem. SWIV synthesizes incomplete knowledge into structured, permanent artifacts — and remembers everything you've built.

SWIV / SESSION_ACTIVE ACTIVE
Topic B2B SaaS content strategy
Entry Drop + Search
Sources 4 added · 2 searched

Q2 content audit.pdf · 38 entities extracted
LinkedIn trends search · 12 sources fetched
Competitor blog scan · patterns identified

problem-first hooks case study format 6-min read length LinkedIn native Q3 content gap
↳ Artifact generated
Research Document — B2B SaaS Content Trends
Findings · Gaps · Actionable implications · 14 sources cited
How it works

Three ways in.
One thing out.

Every entry point — drop, search, or converse — leads to the same artifact shelf. SWIV meets you where the knowledge already is.

01 — Drop

Drop something in

Upload a script, PDF, doc, or link. SWIV reads the content, extracts entities, and understands the domain — so you can ask what to build from it.

02 — Search

Search something

Ask a question or research direction. SWIV fetches sources, extracts transcripts, identifies patterns, and accumulates findings you can refine at any time.

03 — Converse

Converse about it

Talk through a problem, process, or decision. The AI asks probing questions, surfaces gaps, and validates logic — together you build shared understanding that becomes an artifact.

Artifact types

The output is determined
by your intent, not your role.

Any user can need any artifact depending on what they're doing that day. A researcher, an ops manager, and a founder are all one session away from any of these.

Research Document

Understand and communicate what you've found

"I need to understand or communicate something I've researched"

Findings organized by theme, entities extracted from sources, evidence with citations, gaps explicitly called out, and actionable implications. Built from your sessions, not from scratch.

Workflow

Document how something works or should work

"I need to show how a process works"

Phases, steps, decision branches, edge cases, role assignments, and thresholds — capturing the actual process, not the idealized manual version. Built through conversation, not assumption.

Decision Record

Justify and preserve a choice you've made

"I need to justify or document a choice"

Options considered, tradeoffs, recommendation, rationale, and constraints. In six months when circumstances change, you'll know exactly why the decision was made — and whether it still holds.

Why it's different

Built for people who
can't afford to start over.

Every tool tells you to write something, search something, or decide something. SWIV is the first tool that compresses all three — and remembers everything you built.

F.01

You never start from zero

Drop in anything incomplete — a half-written brief, a folder of competitor links, a process you know but haven't written down. SWIV builds outward from your context, not from a blank page.

F.02

The AI nudges, never decides

When enough signal exists, SWIV surfaces a nudge. One tap accepts, one tap dismisses. Nothing is auto-applied, auto-restructured, or silently changed. You are always the author.

F.03

Every artifact feeds the graph

The moment you accept an artifact, its entities and relationships are committed to your context graph. Next time you research similar topics, your history accelerates your work.

F.04

Full source traceability

Every synthesized claim is tagged with the source chunks it came from. If the AI surfaces a pattern, you can click through to the evidence. Nothing is asserted without provenance.

F.05

Sessions are permanent workspaces

No save buttons. No session timeouts. No lost work. A research session is a permanent workspace you return to whenever you need to — add sources, refine findings, or revisit decisions.

F.06

Works day one, compounds over time

No integration setup. No historical data required. A solo creator gets full value on their first session. By month six, SWIV knows your niche's patterns, your approval thresholds, and your decision criteria.

The real moat

It's not the artifacts.
It's what they remember.

Artifacts can be copied. AI capabilities are commodity. The moat is organizational specificity that compounds.

After six months, SWIV knows your niche's emerging trends, your org's actual processes, your approval thresholds, your recurring problems, and your decision patterns. No competitor can replicate this without being embedded in your work for months.

The flywheel: first artifact accepted → graph starts forming → second artifact recognizes context from first → speed accelerates → the knowledge lives here now.

W1

First artifact accepted

Entities committed to graph. Concepts, decisions, and sources begin forming a record.

M1

Context surfaces for the first time

"You researched this niche in week one — here's what's changed." The graph starts earning its keep.

M3

Patterns emerge across sessions

Decision criteria, recurring problems, approval thresholds — SWIV surfaces them before you ask.

M6

Institutional memory you can't leave behind

The organizational knowledge lives in SWIV. What your team researched, decided, and documented — permanent and compounding.

Differentiation

Every other tool solves
half the problem.

Tool What they do What SWIV does differently
Notion AI Improve what you write — still requires a blank page to start Builds outward from incomplete context. You never start from zero.
ChatGPT / Claude Answer one question well, then forget everything Every session builds the graph. Last month's research accelerates this month's.
Miro / Eraser Diagram generators — assume you already know the full process Asks questions to surface the process you couldn't fully articulate. Then validates it.
Transcript tools Summarize individual videos — user still has to synthesize patterns manually Fetch, extract, synthesize, and integrate findings back into your document in one session.
Interloom Enterprise workflow automation — requires IT buy-in, historical data, top-down rollout Works day one with zero historical data. From one freelancer to a 500-person team.
From early users
"I dropped in three competitor links and a half-finished brief. Twenty minutes later I had a research document I could actually send to the client. I've never produced something that structured that fast."
Ayesha R.Content strategist, B2B SaaS agency
"We used SWIV to document our vendor onboarding process by just talking through it. The AI kept asking things we hadn't considered — edge cases, who handles approvals when someone's on leave. The workflow that came out was more accurate than anything we'd written before."
Marcus T.Operations manager, 80-person startup
"The traceability is what got me. Every finding in the research document links back to a source chunk. I can audit it. I can defend it. That's something no chat tool has ever given me."
Priya N.Product manager, Series B

Stop rebuilding from scratch.
Let knowledge compound.

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