We start with the process, not the model
Roughly half of what arrives described as an AI problem turns out to be a data-entry problem with an AI-shaped budget attached. We will tell you which one you have before quoting.
AI softwareengineering
We build the AI systemsyour business runs on.
CoreNexes designs, ships and operates production AI for companies that already have a product and a problem: automation, localization, translation, testing, development and internal tooling, handed over with the source.
Running
- 6
- AI service lines
- Automation to custom tooling
- 24h
- Response time
- On every enquiry, weekdays
- 100%
- Code ownership
- Transferred to you at handover
- NDA-ready
- Your cloud or on-premise
- Source handed over
- Senior engineers only
- Fixed-scope quotes
- 24h response
Why teamscall us
AI projects rarely failon the model.
They fail on everything around it: the process nobody mapped, the hand-offs nobody automated, and the operational work that starts the day the demo ends.
The pilot never became a product
A model that answered well in a demo has been stuck at 80% for two quarters, because nobody scoped retrieval freshness, evals, fallbacks or a cost line.
People are the integration layer
Staff copy data between systems that were never connected. The work scales exactly with volume, appears on no roadmap, and shows up in every headcount request.
Quality is discovered by customers
The test suite is assumed to be flaky, the localized build ships with English strings, and both get found by the people you least want finding them.
Ourservices
Six service lines,each with a named lead.
Not a menu of everything we'd be willing to attempt. Each of these is a standing practice with an engineer who owns it end to end.
AI Automation
Turn repetitive operational work into monitored pipelines.
- Document intake and extraction
- Workflow orchestration
- Human-in-the-loop approvals
AI Localization
Ship one product into every market without a release train per locale.
- Continuous string extraction
- Locale-aware layout QA
- Translation memory as code
AI Translation
Machine translation you can put in front of customers.
- Per-pair engine benchmarking
- Terminology and tone enforcement
- Automated quality estimation
AI Testing
A test suite your team actually trusts.
- Path-derived end-to-end coverage
- Flake detection and quarantine
- Visual regression
AI Development
Model-backed features built like the rest of your product.
- Retrieval and context pipelines
- Prompts and evals in CI
- Guardrails and fallbacks
AI Tools
Internal tools for the workflow your team actually has.
- Review and annotation surfaces
- Operations consoles
- Model-assisted internal search
WhyCoreNexes
A supplier you can judgebefore you hire.
Four commitments that shape every engagement, and that you can hold us to in writing.
Production-first, not demo-first
Anything we build is instrumented, has defined failure behavior, and comes with a runbook. A feature that only works in a meeting is not finished.
You own everything
Work happens in your repository where possible, and every engagement ends with source, deployment and documentation transferred. Lock-in is a business model, not an engineering outcome.
Senior engineers, no account layer
The person who scopes your project writes the code and answers the phone when something breaks. Nothing is handed to a junior team after the sales call.
How wework
From first call to productionin weeks, not quarters.
Four stages, in this order, on every engagement. The order matters more than the speed. Most of what goes wrong in this work goes wrong because someone built before they watched.
- 01Week 1
Discovery
We map the process as it actually runs, agree what success looks like in writing, and identify the constraint worth removing first.
hands over- Process map
- Written definition of done
- Fixed scope and quote
- 02Week 1–2
Architecture
We design the system around your stack, data residency rules and release cadence, then review the plan with your engineers before writing anything.
hands over- Technical design
- Integration plan
- Risk and cost model
- 03Week 2–8+
Build
Two-week cycles with something demonstrable at the end of each. The narrowest useful version reaches production early and is instrumented from the first deploy.
hands over- Working software in your repo
- Tests and CI
- Fortnightly demos
- 04Ongoing
Deploy & hand over
Production rollout with monitoring, a runbook, and a support window while your team takes ownership. An optional retainer follows only if you want one.
hands over- Deployment and monitoring
- Runbook and docs
- Handover session
Side byside
How this work is usually bought,and how we sell it.
The same questions come up on every first call. One column is the arrangement most teams have had before; the other is the one we work under, written down before anything is built.
The usual engagement
How CoreNexes works
Who writes the code
A senior scopes the work, a junior team delivers it
The engineer who scoped it builds it and answers the phone
What ships first
A demo that impresses, then a long road to production
The narrowest useful version, in production and instrumented
Where the code lives
A vendor repository you get a copy of at the end
Your repository, from the first commit where possible
When it goes wrong
Undefined failure behavior, discovered by your customers
Defined fallbacks, alerting and a runbook written before launch
Scope
Widens reasonably, one request at a time
Written definition of done, agreed before anything is built
At handover
A support contract you cannot really leave
Source, deployment and docs transferred; the retainer is optional
Cost visibility
Token spend discovered on the first month's invoice
Cost and latency per stage, modeled in architecture and monitored
Sixsectors
Where this workpays for itself fastest.
The pattern repeats across sectors: high-volume, judgment-light work done by people who are better used elsewhere.
SaaS & Technology
Model-backed features inside an existing product, plus the localization pipelines that let one release reach every market.
- In-product AI features
- Continuous localization
- Test automation at scale
E-commerce & Retail
Catalog translation, product data enrichment, and automated merchandising work that currently runs on spreadsheets.
- Catalog translation
- Product data enrichment
- Support automation
Logistics & Supply Chain
Document-heavy operations, from bills of lading to customs paperwork and proof of delivery, turned into monitored extraction pipelines.
- Document extraction
- Exception handling
- Ops consoles
Healthcare & Life Sciences
Clinical and administrative document processing under real access controls, with audit trails and human review built in.
- Records processing
- Compliance workflows
- Internal search
Financial Services
Back-office automation where being wrong is expensive: reconciliations, onboarding checks and reporting with a full audit trail.
- KYC document review
- Reconciliation
- Regulatory reporting
Media & Publishing
High-volume content localization and metadata enrichment, with quality estimation deciding what a human needs to read.
- Content localization
- Metadata enrichment
- Archive search
Technologystack
Built on tools your engineersalready know.
Chosen per engagement and weighted toward what your team can maintain after handover. Nothing here is imposed on your codebase.
Frontend
05- React
- Next.js
- TypeScript
- Tailwind CSS
- React Native
Backend
06- Python
- FastAPI
- Django REST
- Node.js
- PostgreSQL
- Redis
AI & ML
03- OpenAI
- Anthropic
- vLLM
Infrastructure
06- AWS
- Google Cloud
- Docker
- Kubernetes
- Temporal
- GitHub Actions
Need something outside this list? See the full stack. Or ask, and we'll tell you honestly if it isn't our ground.
Engagementmodels
Three ways to engage,the same team behind each.
Which one fits depends on how settled the work is, not on how big it is. All three run with named engineers, and all three leave the source with you.
Project build
Fixed scope, fixed price
Best forA defined outcome with a deadline
We agree the scope and definition of done up front, then deliver against it. Best when you know what you need and want a predictable number.
- Written scope and acceptance criteria
- Fortnightly demos
- Full source handover
- 30-day post-launch support
Dedicated retainer
Most chosenMonthly, capped hours
Best forOngoing work with shifting priorities
A set number of senior engineering hours each month, prioritized by you. Best once something is live and needs to keep evolving.
- Named engineers, consistent team
- Priorities you control monthly
- Monitoring and incident response
- Rolling 30-day terms
Embedded team
Per engineer, monthly
Best forExtending an in-house team
Our engineers work inside your process: your repo, your standups, your review. Best when you have capacity gaps rather than a discrete project.
- Works in your tooling and rituals
- Direct access to your team
- Knowledge transfer as you go
- Scale up or down quarterly
Scope and price are agreed after the discovery call, once we have seen the process. Nothing above is a quote.
Theteam
The engineersbehind CoreNexes.
Every service line has a named lead. That person scopes the work, builds it, and is who you speak to when something breaks.
Mustafa Tariq
AI Engineer
Lahore, PK
Builds the automation and custom model work: retrieval, evaluation harnesses, and the pipeline that has to keep running after the demo. Decides what ships thin and what waits until it can actually be operated.
Abdul Rehman
AI Engineer
Lahore, PK
Language pipelines end to end: extraction, engine routing, quality estimation, and the release gate at the end of it. Decides where a human still needs to read the output.
Haider
UI/UX Designer
Lahore, PK
Internal tools and the interfaces people sit in all day. Optimizes for throughput and for the tenth hour of use, not for the screenshot.
Anees
Full Stack Developer
Lahore, PK
Ships features from schema to CSS, then owns the test automation around them. Treats flake as a defect rather than weather, and builds suites teams still believe on a bad day.
Commonquestions
The questions a first callusually starts with.
How do you price an engagement?
Fixed scope and fixed price for a project build, monthly for a retainer or an embedded engineer. You get the number after the discovery call, not before. Quoting a system we have not scoped is how a project ends up renegotiated in month three.
What happens to the code when we're finished?
It is yours, and in most cases it was yours the whole time: we work in your repository where we can. Every engagement ends with source, deployment configuration and documentation transferred. There is no runtime license and nothing to keep paying for.
Can you work in our cloud, or on-premise?
Yes. We deploy into your AWS, GCP or Azure account, or on-premise where data residency requires it. Where a hosted model is not acceptable we build against open-weight models running on infrastructure you control.
How small a project will you take?
One workflow. That is usually the right size to start: a narrow, well-instrumented pipeline in production tells you more about whether the wider program is worth funding than any pilot does.
We already have engineers. What do you actually add?
Usually the parts your team has not had to build before: retrieval that stays fresh, evaluation harnesses, confidence gates, and the operational scaffolding around a model. On an embedded engagement we work inside your process and hand that knowledge over as we go.
How quickly can you start?
Discovery inside a week of the first call, and something demonstrable in production within a few weeks after that. We reply to every inquiry within 24 hours on weekdays, including the ones we turn down.
What if we're not sure this is an AI problem?
Then say so on the call. Roughly half of what arrives described as an AI problem turns out to be a data-entry problem with an AI-shaped budget attached, and we would rather tell you that in the first 30 minutes than bill you to discover it.
Starthere
Tell us the process,not the solution.
The most useful first message describes what someone on your team does by hand today and how often. That is enough for us to tell you whether it is worth building.
What happens next
- A named engineer reads it, not a form inbox
- Reply within 24 hours, even if we're not the right fit
- A 30-minute call to trace the process end to end
- A fixed-scope quote, or an honest no