I Built an AI That Remembers What Competitors Did Months Ago

lahari sadimala — Tue, 29 Sep 2026 10:24:34 +0000

I’ve used quite a few AI tools where I ask a question, get an answer, and then move on.

That made me think about something.

What if the AI actually remembered?

Not just the conversation from five minutes ago, but things that happened weeks or months ago and could bring that information back when it became useful.

That question led me to build Competitive Memory.

The idea is pretty simple.

An AI competitive analyst that remembers what competitors have done and uses that history to understand what they are doing now.

-> The Problem I Wanted to Solve

Competitive intelligence sounds simple from the outside.

You track competitors.

You look at their products.

You watch pricing.

You follow announcements.

You keep an eye on their messaging.

But there is a problem that became obvious to me while thinking about this.

There is a lot of information, but the historical context is easy to lose.

Imagine this.

A competitor changes its pricing today.

You can probably find the announcement.

But then you might want to know:

Did they do something similar before?

When did the first signs of this change appear?

What happened after the previous change?

Did their product direction change around the same time?

Are there other events that connect to this?

What do we not know yet?

That is where a normal summary is not enough.

You need memory.

So I Built Competitive Memory

The basic idea behind the project is:

Competitive Event
↓
Remember it
↓
Recall it later
↓
Compare with new events.
↓
Find patterns
↓
Generate insight

The interesting part for me was not just building another chatbot.

I wanted the AI to be able to look at something happening today and say:

“Wait, this looks similar to something that happened before.”

That is where Hindsight comes in.

Why Hindsight?

Hindsight is the memory layer behind the project.

Instead of treating every interaction as completely new, I use Hindsight to retain relevant competitive information and recall it later.

For example, imagine the system has seen this over time.

January: Competitor changes pricing.

March: Competitor launches a new enterprise feature.

May: Competitor changes its messaging.

July: Competitor changes pricing again.

If I only look at July, I see a pricing change.

If the system remembers the previous events, I can ask:

“Have we seen this before?”

Now the July event has context.

That is the part of Hindsight that I found most interesting.

It is not just:

“Store this information.”

It is:

“Remember this because it might matter later.”

The Feature I Liked the Most: We've Seen This Before

While building the idea, one feature kept standing out to me.

I called it:

We've Seen This Before

The idea is that when a new competitor event appears, the system can look through its previous memory and find related events.

So instead of just getting:

“Competitor X changed its pricing.”

you can investigate:

“Have they made a similar move before?”

and then:

“What happened last time?”

That is a much more interesting conversation.

The AI is not only describing the present.

It is connecting the present to the past.

Looking at the Competitive Timeline

Another part of the project is the competitive timeline.

This sounds simple, but I think it changes how you look at the information.

Instead of seeing isolated events:

Pricing change
Product launch
Messaging change
Hiring
Partnership
Pricing change

you can see them as a sequence.

And sequences can tell you things that individual events cannot.

For example, maybe a competitor starts focusing more heavily on enterprise customers.

You might see:

Enterprise messaging
↓
Enterprise features
↓
Enterprise hiring
↓
Enterprise pricing
↓
Enterprise partnerships

None of these events necessarily proves a strategy change on its own.

But together, they give you something worth investigating.

That is what I wanted Competitive Memory to help with.

“What Changed?”

One of the questions I designed the system around is:

“What changed?”

Instead of simply asking the AI for the latest competitor news, I wanted it to compare recent activity with what it already remembers.

That means looking at changes in things like:

Pricing

Products

Features

Messaging

Partnerships

Hiring

Market focus

The goal is not to have the AI confidently guess what a competitor is thinking.

It is to surface changes and give the user enough context to investigate them.

Strategy Shift Detection

Another important capability is identifying potential changes in competitive strategy.

Instead of focusing only on the latest announcement, the system looks across historical activity.

For example, imagine a competitor gradually moving from:

Developer-focused messaging
↓
Enterprise features
↓
Enterprise hiring
↓
Enterprise pricing
↓
Enterprise partnerships

Each event might appear ordinary.

Together, they could represent a meaningful change in direction.

Competitive Memory uses historical context to surface these kinds of patterns for further investigation.

The important distinction is between detecting a possible pattern and claiming that the pattern is definitely a fact.

The system should provide evidence and context so that an analyst can investigate the possibility.

Finding the First Signal

Another question I found interesting was:

“When did this actually start?”

Sometimes the biggest announcement is not the beginning of a change.

There might have been smaller signals beforehand.

For example:

Hiring change
↓
Messaging change
↓
New feature
↓
Product announcement
↓
Pricing change

If you only look at the final announcement, you might miss the earlier signals.

With historical memory, you can look backwards and ask:

“What was the first sign of this?”

That was one of the reasons I wanted memory to be a core part of the product rather than just something running quietly in the background.

“What Happened Last Time?”

This is another question the system is designed around.

If the AI finds something similar in its memory, the next thing you naturally want to ask is:

“What happened last time?”

This is where historical context becomes useful.

Instead of just finding an old event, the system can bring that event back into the current analysis.

So the conversation becomes:

What happened?

Have we seen it before?

What happened last time?

What changed afterward?

That feels much closer to how I would actually want to use a competitive intelligence assistant.

The Idea of a Competitive Time Machine

One of the concepts I enjoyed building around was a Market Time Machine.

Instead of asking:

“What is happening now?”

you can ask:

“What did the competitive landscape look like six months ago?”

Or:

“What happened after the previous pricing change?”

The past becomes something you can actually interact with.

That is possible because the system has retained historical information rather than treating every analysis as a fresh start.

But I Did Not Want the AI to Pretend It Knows Everything

This was an important part of the project for me.

Competitive analysis can easily become speculation.

If an AI sees three events, it might be tempted to confidently tell you what the competitor’s strategy is.

I did not want to design it that way.

So the system separates information into things like:

Observed Fact

Something directly supported by the available information.

Inference

Something that can reasonably be concluded from multiple pieces of evidence.

Hypothesis

A possible explanation that still needs validation.

Unknown

Something we simply do not have enough information to answer.

For example:

Fact:

The competitor changed its enterprise pricing.

Inference:

Recent activity suggests increased focus on enterprise customers.

Hypothesis:

The pricing change may be intended to accelerate enterprise adoption.

Unknown:

The actual reason for the pricing change is not known.

I think that distinction is important when building AI systems for real decisions.

“What Are We Missing?”

One of the questions I really wanted the system to answer was:

“What do we not know?”

Because having a lot of information does not necessarily mean you have enough information.

For example:

Known:
Pricing changed
New feature launched
Messaging changed

Still unknown:
Customer response
Reason for the pricing change
Whether the change is temporary
Whether competitors responded

Instead of giving the user another giant summary, the AI can point out these gaps.

That makes the next research step much clearer.

Evidence Matters

Another thing I wanted to avoid was having the AI make a statement without showing where it came from.

If the system identifies a potential pattern, I want the user to be able to follow the reasoning back to the relevant events.

The basic idea is:

Insight
↓
Relevant events
↓
Historical memory
↓
Evidence

This also leads to another useful question:

“Can we disprove this?”

If the system thinks something might be happening, it should also be possible to look for information that contradicts that idea.

I think this is much more useful than simply asking an AI to confirm whatever conclusion it generated first.

Building the Interface

For the UI, I deliberately did not want the typical dark, neon AI dashboard look.

I wanted something that felt more like a professional research tool.

The interface focuses on:

A clean timeline

Competitor activity

Historical events

Evidence

Patterns

Insights

Memory

The idea is that the user should understand the important information quickly without having to navigate through a huge enterprise dashboard.

The main flow I wanted was:

Event → Memory → Pattern → Evidence → Insight

The Technology Behind It

The project uses a full-stack setup.

The frontend uses React, TypeScript, and Tailwind CSS.

The backend uses Python and FastAPI.

The most important part is Hindsight for persistent AI memory.

The application also uses structured competitive event data so that the demo can reproduce meaningful historical patterns.

For the demo, I use fictional competitors and synthetic data rather than presenting fictional information as real-world competitor intelligence.

That makes the project reproducible while still allowing the memory and reasoning workflow to be demonstrated.

What Made This Project Different for Me

The biggest change in how I thought about AI while building this was realizing that memory is only useful when it affects something later.

It is easy to say:

“Our AI has memory.”

But I think the more interesting question is:

“What can the AI do now because it remembered something from before?”

That is the question I kept coming back to.

If an event from six months ago can help explain an event today, then that memory has real value.

If the AI can recognize a recurring pattern because it remembers previous events, that is useful.

If it can tell me that something is still unknown because it remembers what evidence I already have, that is useful too.

From “What Happened?” to “Why Does This Look Familiar?”

That is probably the biggest idea behind Competitive Memory.

Traditional competitive intelligence often starts with:

“What happened?”

A memory-enabled system can start asking additional questions:

“Have we seen this before?”

“When did this start?”

“What happened last time?”

“What changed?”

“What evidence supports this?”

“What contradicts it?”

“What are we still missing?”

Those questions become possible because the system has a history to work with.

What I Learned From Building It

The biggest lesson for me was that building an AI with memory is not really about making the AI remember more things.

It is about making it remember the right things and retrieve them when they become relevant.

A huge memory is not automatically useful.

The useful part is the connection between:

Past information → Current situation → Better reasoning

That is what made Hindsight interesting for this project.

It gave me a way to think about AI as something that can build context over time rather than starting from zero every time.

What Is Next?

There are a lot of directions I would like to explore with Competitive Memory.

For example, the system could eventually work with continuously changing competitive signals and build richer historical profiles of companies.

It could track the evolution of competitor messaging, identify emerging patterns, connect related events, and generate regular intelligence briefs.

But I do not think the core idea needs to change.

The goal is still:

Do not just collect competitive information. Remember it.

Final Thoughts

Competitors change constantly.

Products change.

Prices change.

Messaging changes.

Strategies evolve.

And every new change has some relationship with what happened before.

I built Competitive Memory because I wanted to explore what happens when an AI does not forget that history.

Not an AI that simply gives you another summary.

Not an AI that starts from zero every time.

But an AI that can look at something happening today and say:

“This looks familiar. Here is what happened before.”

That is the idea behind Competitive Memory.

Remember every move. Understand the pattern.

DEV Community: lahari sadimala

I Built an AI That Remembers What Competitors Did Months Ago

“What Changed?”

Hypothesis