About us

We're building the memory layer for AI.

Torqon is a small, focused team from Ahmedabad, India. We got tired of watching AI tools forget critical context mid-conversation, so we built the infrastructure that makes it stop happening.

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<8ms
p95 retrieval latency
measured in production
71%
avg. token reduction
on heavy usage sessions
99.9%
uptime SLA
on paid plans
MCP
native protocol
no custom SDK needed

Why we exist

AI tools forget. That's not a feature. That's a failure.

Every time a context window fills up, an AI assistant loses the thread. It forgets what you're building, what decisions you already made, what constraints you care about. You end up re-explaining yourself constantly.

Torqon is the infrastructure that makes forgetting stop. We store what matters, retrieve only what's relevant, and deliver it before the model ever touches the request.

Our approach

We don't believe in dumping everything into a massive context and hoping the model sorts it out. That's expensive, slow, and it dilutes quality.

Instead, Torqon stores facts atomically, ranks them by relevance at retrieval time, and assembles only what the current request actually needs. The result is a shorter, denser, more useful context — every single time.

We built this for developers. It connects via MCP, installs in minutes, and stays completely out of your way.

How it works

Three operations. One clean interface.

Atomic fact storage

When your AI tool learns something important, Torqon captures it as a discrete, searchable fact — not a blob of text. Each memory gets embedded and indexed immediately.

Supports any content: decisions, preferences, project context, technical constraints. Works across sessions, across tools, across models.

// store_memory call
fact"Using PostgreSQL, pgvector ext."
typetech_stack
projecttorqon-api
? storedmem_8f3a2c
indexed<3ms

Relevance-ranked retrieval

Before a request reaches the model, Torqon runs a semantic search over your stored memories and returns only the most relevant facts — ranked, filtered, ready to inject.

No full-context dumps. No irrelevant noise. Just the right facts for this specific request, surfaced in under 8ms at p95.

// retrieve_context call
query"what database are we using?"
limit5
1.PostgreSQL + pgvector 0.96
2.Prisma ORM 0.88
3.Railway deployment 0.71

Token budget optimization

Torqon can rewrite and compress your retrieved context to fit within a token budget — removing redundancy, merging related facts, stripping formatting noise.

Average 71.85% token reduction in heavy-usage sessions. The model sees a smaller, cleaner input and responds with better quality.

// optimize_context
input_tokens4,200
budget1,200
? output1,178 tokens
reduced71.95%
latency6ms

What we believe

Four convictions that drive every decision we make.

01 Memory makes AI useful, not just impressive
A language model that can't remember what you told it ten minutes ago is a toy. The benchmark scores don't matter if the tool forgets your project name mid-conversation. Persistent memory is the difference between a demo and a product people rely on every day.
02 Context is intelligence, not just retrieval
RAG retrieves. Torqon reasons about what belongs in context. Flooding a context window with everything that looks similar isn't smart — it's lazy. Real intelligence means knowing which three facts are load-bearing for this specific request, and surfacing only those.
03 Token waste is a solvable engineering problem
Teams accept that AI costs scale with usage. They shouldn't. Every redundant fact in a context is money out the window. We've cut token consumption by 71.85% in real production workloads without losing a meaningful bit of response quality. This is not a trade-off — it's just good engineering.
04 Infrastructure should be invisible
You should never think about Torqon while you're working. Connect via MCP, set one environment variable, and it fades into the background. We measure success partly by how rarely users need to interact with us directly — the memory just works, and so does your AI.

Our story

How we got here

Torqon wasn't planned. It was built out of frustration — the kind that happens when you're deep in a project and your AI assistant asks you to repeat something you explained forty minutes ago.

May 2025
Started building
Frustrated by AI tools that forgot everything between sessions, we started building the infrastructure to fix it. No product yet — just a clear problem and a conviction.
September 2025
First prototype
Built a working store-retrieve loop over a vector database. The context quality improvement was immediate and obvious. We knew we were onto something real.
February 2026
Token optimization breakthrough
Added the optimization pass. First time we hit 70%+ token reduction without degrading response quality. That number became the one we obsessed over.
May 2026
MCP-native, public launch
Rebuilt the integration layer around the Model Context Protocol. One config block, one npx command — memory works in Claude Desktop, Cursor, Heym, and more.
Now
Growing with our users
Torqon is live in production for developers across the world. We're expanding integrations, hardening the platform, and building toward Teams and Enterprise.

The team

Small team, full conviction.

M
Maan Ahir
Founder
Building Torqon out of Ahmedabad, India. Obsessed with context, latency, and making developer tools that actually stay out of the way.

Get in touch

Let's talk

Whether you're evaluating Teams, need help with your integration, or just have a question — we respond within one business day.

Location
Ahmedabad, India
Response time
Within 1 business day
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