Software Founders & Builders

Personalization built into your product, not a third-party tag

We build recommendation engines and content ranking into your own data layer. Users see what fits them. You own the model.

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The problem

Sound familiar?

Every user sees the same content in the same order

A new user and a power user see the same homepage. One ignores what they already know. The other drowns in options.

Third-party widgets share your user data with competitors

Plug-in tools train one shared model across all their customers. Your data helps a model that also serves your competitors.

Cold-start leaves new users with no relevant recommendations

Collaborative filtering fails when a user has no history. Without a cold-start plan, the first session is as generic as no personalization at all.

You cannot prove personalization improves retention

Without A/B testing built into the serving layer, you cannot tell if the model helps or just shuffles things around.

The solution

What we actually do

We track your product events, build a feature store, train a recommendation model, and ship an API your frontend calls. No widgets, no data sharing, no black box.

What you get

What's included

Event pipeline for clicks, dwell time, and conversions

Feature store with user profiles and item embeddings

Recommendation model, collaborative or content-based, to fit your data

A/B testing framework with statistical significance tracking

Serving API returning ranked results under 100ms at P95

Monitoring dashboard for CTR, conversion lift, and drift

Docs and retraining runbook so your team can iterate

The process

How it works

Instrument

We audit your event tracking, fill the gaps, and define the signals that train the model.

Model

We build the feature store and train a model on your data, using the method your data supports today.

Serve

We deploy a serving API and wire it to your frontend. Ranked per user, cached, under 100ms.

Optimise

We A/B test against the baseline, measure CTR and retention lift, and retrain on a schedule.

Proof it works

Pack Assist

8-week delivery, RAG + hybrid AI

Read the case study

The offer

Scoping call required. Most builds ship in 6 to 12 weeks, depending on your data.

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Common questions

Frequently asked

01What if we do not have much historical data yet?

We design a cold-start plan using content signals and onboarding inputs. New users get relevant results from session one.

02Do you use collaborative filtering or content-based ranking?

Both. We pick based on your data volume and catalog. A hybrid works best when your catalog is large and your user base is growing.

03How long until we see a measurable lift in retention?

Most products see CTR gains within the first two sprints of A/B testing. That is usually two to four weeks after the API goes live.

04Can this integrate with our existing data warehouse?

Yes. We build the feature store on what you have: BigQuery, Redshift, Snowflake, or Postgres.

05Will we be able to retrain the model ourselves after handoff?

Yes. We document the retraining pipeline and walk your team through it before handoff. You own the model and the infrastructure.

06Is TechEmulsion based offshore?

No. We operate through our US entity in Wyoming. Our team works in your timezone.

Ready to get started?

Let's build your ml-driven personalization system

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