Contextfold Start a conversation
The product layer for AI companies

Built for the layer between the model and the customer

We design the context your agent sees and what it remembers.

Start a conversationSee the practice
Same layer, any domain. The context is yours.
Worked with
  • Quizizz
  • Wayground
  • UpGrad
  • Loopin
  • Venture Highway
  • Quizizz
  • Wayground
  • UpGrad
  • Loopin
  • Venture Highway
  • Quizizz
  • Wayground
  • UpGrad
  • Loopin
  • Venture Highway
The practice

Where AI products are won

Four things that separate a demo from a product people depend on.

Sales
Account memorySourceUsed
Northwind evaluates in Q1, never signs in Q4AE note2h ago
Procurement must see security review firstClosed-won1d ago
Acme champion moved to Beacon HealthCorrection3d ago
Discount above 15% needs VP sign-offPricing rules8d ago
Send decks as PDF, portal links get ignoredAE note12d ago

Context that compounds

The right sources, in the right order, plus what your users teach the product every day.

Customer support
Understand→Ask→Answer→Act→Check
One question firstThis account cancelled 9 days into an annual plan. Policy allows partial refunds. Which do you want?
Full refundPro-ratedCredit toward monthly

A harness people trust

Asks before it acts, shows its reasoning, and is measured by evals written in your customer's terms.

Finance ops
Everything in
38,400 tokens
Right context
9,100 tokens
−76%tokens per answer
2.1×faster
+11 ptsaccuracy

Right context, lower cost

Stuffing the window makes the model worse and the bill bigger. Retrieve what matters, and accuracy, latency and cost all move the right way.

Recruiting

Relevance you can measure

How often people accept the answer as given, tracked every release. The leading indicator of whether they come back.

Same model. Different answer.

The only thing that changed is what the model was given.

RequestWhich customers are likely to churn this quarter, and what should we do about each?
ModelAny frontier model
What the model actually sees
Your promptOne paragraph
This threadGone when you open a new onePlus every prior session
MethodHow your team defines churn risk
Your data, retrievedUsage, tickets, renewals, CSM notes. Cited.
MemoryStanding facts, corrections, decisions
ToolsCRM, billing, email
General trainingPublic knowledge to a cut-off. Identical for every competitor. Knows nothing about your customers.
The loop
Reason→ Call a tool→ Check against the spec→ Write a reply, once Repeat until complete
What you get
A paragraph in the thread. Plausible, uncited, forgotten.
A churn list with a reason and next step per account, each line cited. Saved, shared, rerun every Monday.
Confirmed facts return to memory
Why build it yourself

The model is rented. The context layer is yours.

Everyone gets the same model. Nobody gets your customers' knowledge unless you build the layer that captures it.

Knowledge base

How each customer actually works, structured so the agent can use it.

Runtime memory

Corrections, preferences and decisions, captured as people use the product. Compounds with every interaction.

Evals

Your definition of a good outcome, in the customer's terms, measured on every release.

Rented, same for everyone:ModelsTool callingVector databasesAgent frameworks

How we work

Embedded in your team, scoped to the problem, with a clean handoff.

1

Diagnose

Two weeks with your product, team, and customer data. You get a written diagnosis and a plan.

2

Embed

Specs, evals, and UX decisions ship alongside the engineers building them, inside your rhythm.

3

Hand off

Everything documented and owned by your team. Advisory time afterwards if you want it.

Sprint

One sharply scoped problem with a written outcome.

Context architecture, eval suite, harness redesign

Embedded

Our team inside your team, from pilot to paying customers.

Roadmap, specs, release cadence

Advisory

A standing session with the founder or product lead.

Product and GTM calls, review of specs and evals
Work

How teams work with Contextfold

Embedded · Context and harness

Teaching an operations agent what experienced operators know

Plans were only as good as the knowledge base, and the knowledge base was a fraction of what the team knew. We designed runtime capture of operator expertise and rebuilt the harness from the operator's side.

Outcome: higher-accuracy plans, approved instead of bypassed.
Advisory · Adoption and fit

Turning sign-ups into weekly use for corporate development teams

Strong top-of-funnel, weak week-four retention. A retention thesis built on workflow embedding and ingestion, shipped in three-week releases.

Outcome: a sprint rhythm and a retention thesis the team tests, not argues about.
Product lead · Enterprise

Agent-building for the enterprise on permissioned knowledge

Led product for an enterprise agent-builder platform over permission-aware retrieval and a company knowledge graph.

Carried forward: the context and lifecycle patterns we apply to early-stage products.
15+ yrs
Shipping high-quality products across domains
Since 2021
Shipping AI assistants and agents, as founders and as enterprise product leads
2 wks
To a written diagnosis you keep, before any longer commitment

Where is your product stuck?

Tick what sounds like your product today.
Nothing ticked yet.Either excellent news, or a sign nobody has looked closely.

Tell us where the demo stops working

A forty-minute call, no deck. Bring the product and the thing that keeps breaking.

Email hello@contextfold.aiBook a call
Bangalore, India · Overlapping US mornings and European afternoons