Custom software & AI development partnerYou think it.
We finish it.

We scope, build and run software and AI products end to end, or put engineers inside the team you already have.

Book a consult See our work

3 days to a written scopeWeek 6 first release liveSenior-led teams

Hopsig Technologies LLC · Wyoming, USA · Delivery Centre: Lahore, Pakistan

Day 0

The call

You describe the problem. We say on the call whether we are right for it, and what it would cost.

Day 3

The scope

A two-page SOW: what is in, what is out, what it costs. Before you pay anything.

Day 10

People in

Access, first commit. You meet the engineers before they start, and they are in your standup from day one.

Week 6

First increment live

Working software in your environment, measured before we claim it improved.

Fortnightly

The review

Shipped, slipped, cost against plan. Continue, or hand over. Your call, every fortnight.

Scroll ↓ the engagement, in five beats

Agentic delivery

Six agents. A person at every gate.

Our delivery pipeline runs on Claude Code. Agents plan, build, review and document. People sign off. Agents never merge to main.

A story moves through six agents. A person signs at every gate. Agents never merge to main.

Get it in six weeks See the pipeline

Custom software & AI development partnerYou think it.
We finish it.

We scope, build and run software and AI products end to end, or put engineers inside the team you already have.

Book a consult See our work

3 days to a written scopeWeek 6 first release liveSenior-led teams

Hopsig Technologies LLC · Wyoming, USA · Delivery Centre: Lahore, Pakistan

Day 0

The call

You describe the problem. We say on the call whether we are right for it, and what it would cost.

Day 3

The scope

A two-page SOW: what is in, what is out, what it costs. Before you pay anything.

Day 10

People in

Access, first commit. You meet the engineers before they start, and they are in your standup from day one.

Week 6

First increment live

Working software in your environment, measured before we claim it improved.

Fortnightly

The review

Shipped, slipped, cost against plan. Continue, or hand over. Your call, every fortnight.

Scroll ↓ the engagement, in five beats

Agentic delivery

Six agents. A person at every gate.

Our delivery pipeline runs on Claude Code. Agents plan, build, review and document. People sign off. Agents never merge to main.

A story moves through six agents. A person signs at every gate. Agents never merge to main.

Get it in six weeks See the pipeline

Core services

Eight things we do. All of them end in something running.

All services →

01Custom softwareBuilt · fixed scope per incrementDetails →

Products built end to end

A written scope, a fixed first increment, working software in your cloud inside six weeks. Then we keep going, or we hand it over with a runbook.

Scope in 3 daysTwo pages: in, out, cost.

Six-week incrementsFixed scope, working software.

Handover packRunbook, tests, walkthrough.

Good for

— A new product or module with a deadline

— A rebuild the in-house team cannot fit in

— An MVP that has to survive its first customers

Stack · only what we have shipped to production

.NET / .NET CoreNode.jsFastifyPythonFastAPIDjango RESTReactNext.jsAngularTypeScriptPostgreSQLSQL ServerMongoDBRedisGraphQL

Engagement: Built · fixed scope per increment

02Team augmentationEmbedded · monthly, per engineerDetails →

Engineers inside your team

Named engineers in your standup from day one, on your repo and your board. We manage the person; you direct the work. Replacement inside ten working days if it is not working.

Senior-ledA senior on every account; the level fits the work.

Your toolingNothing of ours to adopt.

10-day replacementIf it is not working.

Good for

— Scaling a team without a six-month hiring round

— A nine-month push with a hard date

— Covering a leaver while you hire properly

Stack · only what we have shipped to production

BackendFrontendFull-stackMobileDataProduct.NETNode.jsPythonReactAngularKotlin

Engagement: Embedded · monthly, per engineer

03Data engineering & analyticsData & AI · project or retainedDetails →

Pipelines and reporting people stop arguing with

Ingest, warehouse, models and dashboards that the in-house team can run after we leave. Measured before and after every increment.

Batch → incrementalNightly runs that finish.

One source of truthWarehouse and semantic layer.

Dashboards usedBy the people who asked.

Good for

— Reporting nobody trusts

— A nightly batch that is now a daily problem

— Numbers that differ by who ran the query

Stack · only what we have shipped to production

Custom ETLAWS GlueAthenaPostgreSQLTimescaleDBRedshiftPower BIMixpanelAmplitudeFunnel analysis

Engagement: Data & AI · project or retained

04AI & machine learningData & AI · project or retainedDetails →

Features that have to be right, not impressive

Retrieval, LLM features, classification and forecasting, each with an evaluation harness so you know when it got better and when it did not.

Eval harness firstA number before a demo.

Retrieval done properlyChunking, ranking, citations.

Cost and latencyBudgeted, then measured.

Good for

— An AI feature that has to be right, not impressive

— Search or support that should answer from your data

— Forecasts the finance team will actually use

Stack · only what we have shipped to production

Claude CodeMCP serversLangGraphLangChainCrewAIRAGpgvectorQdrantClaude APIOpenAIGeminiAWS BedrockLangSmithLangfusePromptfooTensorFlowOpenCV

Engagement: Data & AI · project or retained

05Cloud & DevOpsEmbedded or BuiltDetails →

Deploys that are boring

CI/CD, infrastructure as code, observability and cost control, set up so your team owns it afterwards and the on-call runbook is written before you need it.

CI from day oneTests before features.

Infra as codeReviewed like code.

Cost controlA bill you can explain.

Good for

— Deploys that need a war room

— A cloud bill nobody can explain

— No runbook for the thing that pages you

Stack · only what we have shipped to production

AWS EKSECRRDSLambdaSQS / SNSS3CloudWatchAzureDockerCI/CDVercelRailwayNeonCloudflare R2EMQX / MQTT

Engagement: Embedded or Built

06Legacy modernisationBuilt · fixed scope per incrementDetails →

Rebuilds the in-house team can run

Strangler-pattern migrations off the system nobody wants to touch, one bounded piece at a time, with the old and new running side by side until the numbers match.

One seam at a timeNo big-bang cutover.

Parity measuredOld vs new, same inputs.

Knowledge capturedDocs where your team looks.

Good for

— A system nobody wants to touch

— A framework three major versions behind

— A migration that has been ‘next quarter’ for two years

Stack · only what we have shipped to production

Monolith → microservices.NET Framework → .NETIIS / EC2 stabilisationRDS migrationsCron → queuesgRPCGraphQLWordPress → custom

Engagement: Built · fixed scope per increment

07MobileBuilt · fixed scope per incrementDetails →

iOS and Android without two teams

Cross-platform where it makes sense, native where it does not, with the release pipeline and crash reporting set up before the first store submission.

One codebaseWhere the product allows.

Store-readySigning, review, release.

Crash-freeTracked from day one.

Good for

— A web product that needs to be in the stores

— An app with more crashes than reviews

— Two native teams doing the same work twice

Stack · only what we have shipped to production

AndroidKotlinAOSPiOSOffline-first syncEMQX / MQTTTauri (desktop)Electron (desktop)Figma design system

Engagement: Built · fixed scope per increment

08QA & test automationEmbedded · monthly, per engineerDetails →

Suites that make deploys boring

Unit, integration and end-to-end coverage that runs in CI in minutes, plus load testing before the launch rather than after the outage.

Runs in minutesOr it is not run.

Flake trackedAnd fixed, not retried.

Load testedBefore launch day.

Good for

— Releases that break something every time

— A test suite everyone skips

— A launch with no idea what load it takes

Stack · only what we have shipped to production

Automated test generationEval harnessesGuardrailsPlaywrightpytestJestk6CI pipelines

Engagement: Embedded · monthly, per engineer

08 of 08 · the file closes hereAll services →

Some of our work

Where we have done this before.

Embedded payments and stored-value wallet platform

Fintech · payments150+merchants and 100K+ transactionsBuilt · product-led

Problem

Merchants needed to take card, wallet and BNPL payments behind one integration, and onboarding could not depend on a manual review queue.

What we did

Built from zero: self-serve onboarding with automated KYC document verification, KYC-verified adult and child wallets, subscription billing, PCI-DSS compliance, and fraud and anomaly detection across payments and reward balances. Checkout conversion was the metric; payment-method mix decided what got built next.

Measured

0 → 150+ merchants and 100K+ transactions in four months of launch.

PaymentsKYC automationPCI-DSSBNPLWalletsSubscription billing

Measured

150+

merchants and 100K+ transactions, four months after launch

LaunchMonth 4

Read the case note →

Loyalty and retention engine for enterprise retailers

Retail · loyalty5×repeat transactions across enterprise loyalty programmesBuilt

Problem

Enterprise retailers, including a Gulf property group, wanted repeat purchase without reward fraud eating the margin.

What we did

Reward rules, tiers and gamification with fraud-risk mitigation designed in, across enterprise programmes on several continents.

Measured

Up to 5× increase in repeat transactions.

LoyaltyFraud detectionEnterprise rollout

Measured

5×

repeat transactions across enterprise loyalty programmes

BeforeAfter

Ask about this →

Personalisation and image-search engine

Retail · AI261×client ROIData & AI

Problem

Recommendations and visual search that had to prove their return in the client’s numbers, not in a demo.

What we did

Recommendation and image-search models with evaluation before release, production monitoring, retraining and version rollout owned end to end.

Measured

Up to 261× client ROI. Shipped an image-search model into production.

Recommendation systemsImage searchEvaluation gatesModel monitoring

Measured

261×

client ROI, upper bound, from recommendation and image search

Ask about this →

Agentic software-delivery framework on Claude Code

AI · delivery10engineers on the agentic pathData & AI

Problem

An engineering practice wanted to deliver faster without losing review, tests and documentation along the way.

What we did

An end-to-end pipeline on Claude Code: project planning, automated epic and story population with progress tracking, autonomous story implementation, a dedicated solutions-architect agent, guardrail enforcement, automated code review and test generation, living documentation, and human-in-the-loop escalation pushed to mobile when a decision needs a person.

Measured

Rolled out to 10 engineers and now the practice’s default path for new work. Two Android apps scoped at five developers were shipped by one, on the original timeline.

Claude CodeMCP serversAgent orchestrationGuardrailsAutomated reviewJira

Measured

engineers on the agentic path; two apps scoped at five developers, shipped by one

ScopedNeeded

Ask about this →

Inventory, POS and quick-commerce at scale

Retail · omnichannel100+cloud POS locationsBuilt

Problem

Retailers across MENAP needed one stock position, a point of sale that worked in every store, and a mobile channel.

What we did

Distributed inventory across retailers, a cloud POS, and a quick-commerce mobile app that powered a network reaching millions of small businesses.

Measured

Distributed inventory for 60+ retailers; cloud POS in 100+ locations; a mobile channel reaching 10M SMEs across MENAP.

InventoryCloud POSMobile commerceMulti-country

Measured

100+

cloud POS locations; distributed inventory for 60+ retailers

Ask about this →

Preclinical research portal for global labs

Life sciences · data60%less analysis time on preclinical data across global labsData & AI

Problem

Preclinical data was growing exponentially across labs and formats; nobody could compare it, let alone predict from it.

What we did

A compliant cloud architecture with custom ETL to harmonise heterogeneous lab data. Machine-learning models for predicting therapeutic response and model relevance. Dashboards and real-time visualisation for researchers.

Measured

Data analysis time down 60%. 100% of data visible across global teams. Predictive accuracy up.

Custom ETLPythonML modelsCloud architectureDashboards

Measured

60%

less analysis time on preclinical data across global labs

BeforeAfter

Read the case note →

Real-time operational intelligence for 200+ buildings

Buildings · IoT200+buildings on one real-time operational telemetry platformProduct

Problem

Building operators needed live operational data, not last month’s report.

What we did

Product leadership of a telemetry platform delivering real-time operational intelligence across a large building estate.

Measured

Powering 200+ buildings.

IoT telemetryReal-timeOperations

Measured

200+

buildings on one real-time operational telemetry platform

Ask about this →

Organic revenue engine for an online store

E-commerce · growth6×monthly revenue in six monthsGrowth

Problem

A WooCommerce/Shopify store with almost no organic sales.

What we did

Collection-based keyword targeting across product and category pages, AI-assisted content at scale, caching for fast checkout, and CRO on the checkout funnel.

Measured

Monthly revenue from $2,000 to $12,000 in six months — 6×, organic only.

WooCommerceShopifyAhrefsSemrushWP Rocket

Measured

6×

monthly revenue in six months, organic only

$2K$12K

Ask about this →

All 32 case notes →

Start a new build, or add engineers to one that is already running.

Book a consult

What we work in.

Only what we have shipped to production

Backend servicesFrontendData pipelinesWarehousingRetrievalEvaluation harnessesPlatform & CIObservabilityPaymentsAuth & SSOSearchMigrations

Event streamingBatch → incrementalAPI designMobileReportingModel servingCost controlOn-call runbooksData qualityIntegrationsAccess controlLoad & scale

Not on the list? We will say so on the call, rather than learn it on your time.

Why Hopsig.

Scope before money

A written SOW three days after the call, before you pay.

Senior-led

A senior leads every engagement and reviews every change; the rest of the team fits the work and the budget.

Agentic delivery

Our own SDLC on Claude Code: planning, review, tests and docs automated, a person in the loop when it matters.

Easy to leave

Thirty days’ notice, either side, no argument.

Measured

We measure it before we claim it improved.

The record

Shipping to production since 2019.

Payments, regulated healthcare, retail at scale, agentic delivery. Every system on the Work page was built, run or measured by us. Client names are withheld until the call; the numbers are not.

See the record →

150+

merchants and 100K+ transactions, four months after an embedded payments platform launched

5×

repeat transactions from a loyalty engine built for enterprise retail

98%

detection accuracy from a computer-vision product working on field photographs

60%

less analysis time on preclinical data harmonised across global labs

Hopsig Technologies LLC · Wyoming, USADelivery Centre: Lahore, PakistanIn production since 2019Built for teams in the UK, US and EU

Notes

Things we would say on the call anyway.

Short and opinionated, written by the people doing the work.

2 notes · no newsletter, no pop-up

LatestDelivery · 16 September 2026

Superintelligence is not on your roadmap. Agents are.

The word is everywhere this month. Here is what it changes for a sixty-person engineering team: nothing, and one thing.

Read the note →

02The SOW is the sales pitch.Scope · 22 July 2026→

Everything else we have writtenAll notes →

Thirty minutes, and you will know whether we can help.

Bring the problem, not a brief. If it is outside what we do well, we will say which of our contacts to call instead.

Book a consult See our work

Or email info@hopsig.com — reply within one working day.

Core services

Eight things we do. All of them end in something running.

All services →

01Custom softwareBuilt · fixed scope per incrementDetails →

Products built end to end

A written scope, a fixed first increment, working software in your cloud inside six weeks. Then we keep going, or we hand it over with a runbook.

Scope in 3 daysTwo pages: in, out, cost.

Six-week incrementsFixed scope, working software.

Handover packRunbook, tests, walkthrough.

Good for

— A new product or module with a deadline

— A rebuild the in-house team cannot fit in

— An MVP that has to survive its first customers

Stack · only what we have shipped to production

.NET / .NET CoreNode.jsFastifyPythonFastAPIDjango RESTReactNext.jsAngularTypeScriptPostgreSQLSQL ServerMongoDBRedisGraphQL

Engagement: Built · fixed scope per increment

02Team augmentationEmbedded · monthly, per engineerDetails →

Engineers inside your team

Named engineers in your standup from day one, on your repo and your board. We manage the person; you direct the work. Replacement inside ten working days if it is not working.

Senior-ledA senior on every account; the level fits the work.

Your toolingNothing of ours to adopt.

10-day replacementIf it is not working.

Good for

— Scaling a team without a six-month hiring round

— A nine-month push with a hard date

— Covering a leaver while you hire properly

Stack · only what we have shipped to production

BackendFrontendFull-stackMobileDataProduct.NETNode.jsPythonReactAngularKotlin

Engagement: Embedded · monthly, per engineer

03Data engineering & analyticsData & AI · project or retainedDetails →

Pipelines and reporting people stop arguing with

Ingest, warehouse, models and dashboards that the in-house team can run after we leave. Measured before and after every increment.

Batch → incrementalNightly runs that finish.

One source of truthWarehouse and semantic layer.

Dashboards usedBy the people who asked.

Good for

— Reporting nobody trusts

— A nightly batch that is now a daily problem

— Numbers that differ by who ran the query

Stack · only what we have shipped to production

Custom ETLAWS GlueAthenaPostgreSQLTimescaleDBRedshiftPower BIMixpanelAmplitudeFunnel analysis

Engagement: Data & AI · project or retained

04AI & machine learningData & AI · project or retainedDetails →

Features that have to be right, not impressive

Retrieval, LLM features, classification and forecasting, each with an evaluation harness so you know when it got better and when it did not.

Eval harness firstA number before a demo.

Retrieval done properlyChunking, ranking, citations.

Cost and latencyBudgeted, then measured.

Good for

— An AI feature that has to be right, not impressive

— Search or support that should answer from your data

— Forecasts the finance team will actually use

Stack · only what we have shipped to production

Claude CodeMCP serversLangGraphLangChainCrewAIRAGpgvectorQdrantClaude APIOpenAIGeminiAWS BedrockLangSmithLangfusePromptfooTensorFlowOpenCV

Engagement: Data & AI · project or retained

05Cloud & DevOpsEmbedded or BuiltDetails →

Deploys that are boring

CI/CD, infrastructure as code, observability and cost control, set up so your team owns it afterwards and the on-call runbook is written before you need it.

CI from day oneTests before features.

Infra as codeReviewed like code.

Cost controlA bill you can explain.

Good for

— Deploys that need a war room

— A cloud bill nobody can explain

— No runbook for the thing that pages you

Stack · only what we have shipped to production

AWS EKSECRRDSLambdaSQS / SNSS3CloudWatchAzureDockerCI/CDVercelRailwayNeonCloudflare R2EMQX / MQTT

Engagement: Embedded or Built

06Legacy modernisationBuilt · fixed scope per incrementDetails →

Rebuilds the in-house team can run

Strangler-pattern migrations off the system nobody wants to touch, one bounded piece at a time, with the old and new running side by side until the numbers match.

One seam at a timeNo big-bang cutover.

Parity measuredOld vs new, same inputs.

Knowledge capturedDocs where your team looks.

Good for

— A system nobody wants to touch

— A framework three major versions behind

— A migration that has been ‘next quarter’ for two years

Stack · only what we have shipped to production

Monolith → microservices.NET Framework → .NETIIS / EC2 stabilisationRDS migrationsCron → queuesgRPCGraphQLWordPress → custom

Engagement: Built · fixed scope per increment

07MobileBuilt · fixed scope per incrementDetails →

iOS and Android without two teams

Cross-platform where it makes sense, native where it does not, with the release pipeline and crash reporting set up before the first store submission.

One codebaseWhere the product allows.

Store-readySigning, review, release.

Crash-freeTracked from day one.

Good for

— A web product that needs to be in the stores

— An app with more crashes than reviews

— Two native teams doing the same work twice

Stack · only what we have shipped to production

AndroidKotlinAOSPiOSOffline-first syncEMQX / MQTTTauri (desktop)Electron (desktop)Figma design system

Engagement: Built · fixed scope per increment

08QA & test automationEmbedded · monthly, per engineerDetails →

Suites that make deploys boring

Unit, integration and end-to-end coverage that runs in CI in minutes, plus load testing before the launch rather than after the outage.

Runs in minutesOr it is not run.

Flake trackedAnd fixed, not retried.

Load testedBefore launch day.

Good for

— Releases that break something every time

— A test suite everyone skips

— A launch with no idea what load it takes

Stack · only what we have shipped to production

Automated test generationEval harnessesGuardrailsPlaywrightpytestJestk6CI pipelines

Engagement: Embedded · monthly, per engineer

08 of 08 · the file closes hereAll services →

Some of our work

Where we have done this before.

Embedded payments and stored-value wallet platform

Fintech · payments150+merchants and 100K+ transactionsBuilt · product-led

Problem

Merchants needed to take card, wallet and BNPL payments behind one integration, and onboarding could not depend on a manual review queue.

What we did

Measured

0 → 150+ merchants and 100K+ transactions in four months of launch.

PaymentsKYC automationPCI-DSSBNPLWalletsSubscription billing

Measured

150+

merchants and 100K+ transactions, four months after launch

LaunchMonth 4

Read the case note →

Loyalty and retention engine for enterprise retailers

Retail · loyalty5×repeat transactions across enterprise loyalty programmesBuilt

Problem

Enterprise retailers, including a Gulf property group, wanted repeat purchase without reward fraud eating the margin.

What we did

Reward rules, tiers and gamification with fraud-risk mitigation designed in, across enterprise programmes on several continents.

Measured

Up to 5× increase in repeat transactions.

LoyaltyFraud detectionEnterprise rollout

Measured

5×

repeat transactions across enterprise loyalty programmes

BeforeAfter

Ask about this →

Personalisation and image-search engine

Retail · AI261×client ROIData & AI

Problem

Recommendations and visual search that had to prove their return in the client’s numbers, not in a demo.

What we did

Recommendation and image-search models with evaluation before release, production monitoring, retraining and version rollout owned end to end.

Measured

Up to 261× client ROI. Shipped an image-search model into production.

Recommendation systemsImage searchEvaluation gatesModel monitoring

Measured

261×

client ROI, upper bound, from recommendation and image search

Ask about this →

Agentic software-delivery framework on Claude Code

AI · delivery10engineers on the agentic pathData & AI

Problem

An engineering practice wanted to deliver faster without losing review, tests and documentation along the way.

What we did

Measured

Rolled out to 10 engineers and now the practice’s default path for new work. Two Android apps scoped at five developers were shipped by one, on the original timeline.

Claude CodeMCP serversAgent orchestrationGuardrailsAutomated reviewJira

Measured

engineers on the agentic path; two apps scoped at five developers, shipped by one

ScopedNeeded

Ask about this →

Inventory, POS and quick-commerce at scale

Retail · omnichannel100+cloud POS locationsBuilt

Problem

Retailers across MENAP needed one stock position, a point of sale that worked in every store, and a mobile channel.

What we did

Distributed inventory across retailers, a cloud POS, and a quick-commerce mobile app that powered a network reaching millions of small businesses.

Measured

Distributed inventory for 60+ retailers; cloud POS in 100+ locations; a mobile channel reaching 10M SMEs across MENAP.

InventoryCloud POSMobile commerceMulti-country

Measured

100+

cloud POS locations; distributed inventory for 60+ retailers

Ask about this →

Preclinical research portal for global labs

Life sciences · data60%less analysis time on preclinical data across global labsData & AI

Problem

Preclinical data was growing exponentially across labs and formats; nobody could compare it, let alone predict from it.

What we did

Measured

Data analysis time down 60%. 100% of data visible across global teams. Predictive accuracy up.

Custom ETLPythonML modelsCloud architectureDashboards

Measured

60%

less analysis time on preclinical data across global labs

BeforeAfter

Read the case note →

Real-time operational intelligence for 200+ buildings

Buildings · IoT200+buildings on one real-time operational telemetry platformProduct

Problem

Building operators needed live operational data, not last month’s report.

What we did

Product leadership of a telemetry platform delivering real-time operational intelligence across a large building estate.

Measured

Powering 200+ buildings.

IoT telemetryReal-timeOperations

Measured

200+

buildings on one real-time operational telemetry platform

Ask about this →

Organic revenue engine for an online store

E-commerce · growth6×monthly revenue in six monthsGrowth

Problem

A WooCommerce/Shopify store with almost no organic sales.

What we did

Collection-based keyword targeting across product and category pages, AI-assisted content at scale, caching for fast checkout, and CRO on the checkout funnel.

Measured

Monthly revenue from $2,000 to $12,000 in six months — 6×, organic only.

WooCommerceShopifyAhrefsSemrushWP Rocket

Measured

6×

monthly revenue in six months, organic only

$2K$12K

Ask about this →

All 32 case notes →

Start a new build, or add engineers to one that is already running.

Book a consult

Custom software & AI development partnerYou think it.We finish it.

Six agents. A person at every gate.

Custom software & AI development partnerYou think it.We finish it.

Six agents. A person at every gate.

Eight things we do. All of them end in something running.

Products built end to end

Engineers inside your team

Pipelines and reporting people stop arguing with

Features that have to be right, not impressive

Deploys that are boring

Rebuilds the in-house team can run

iOS and Android without two teams

Suites that make deploys boring

Where we have done this before.

Embedded payments and stored-value wallet platform

Loyalty and retention engine for enterprise retailers

Personalisation and image-search engine

Agentic software-delivery framework on Claude Code

Inventory, POS and quick-commerce at scale

Preclinical research portal for global labs

Real-time operational intelligence for 200+ buildings

Organic revenue engine for an online store

What we work in.

Why Hopsig.

Shipping to production since 2019.

Things we would say on the call anyway.

Superintelligence is not on your roadmap. Agents are.

Thirty minutes, and you will know whether we can help.

Eight things we do. All of them end in something running.

Products built end to end

Engineers inside your team

Pipelines and reporting people stop arguing with

Features that have to be right, not impressive

Deploys that are boring

Rebuilds the in-house team can run

iOS and Android without two teams

Suites that make deploys boring

Where we have done this before.

Embedded payments and stored-value wallet platform

Loyalty and retention engine for enterprise retailers

Personalisation and image-search engine

Agentic software-delivery framework on Claude Code

Inventory, POS and quick-commerce at scale

Preclinical research portal for global labs

Real-time operational intelligence for 200+ buildings

Organic revenue engine for an online store

What we work in.

Why Hopsig.

Shipping to production since 2019.

Things we would say on the call anyway.

Superintelligence is not on your roadmap. Agents are.

Thirty minutes, and you will know whether we can help.

Custom software & AI development partnerYou think it.
We finish it.

Custom software & AI development partnerYou think it.
We finish it.