We build what's next. Then we make it hold.
AI, software, cloud and intelligent systems, engineered to run in production rather than to demonstrate well.
The distance between a demo and a system.
Almost anything can be made to work once. The engineering is in what happens next: when the data is messy, when the load arrives, when someone asks where a number came from, and when the person who built it has moved on.
That distance is where JANNEX works. We take problems that are real and slightly too hard — an assistant that has to be right, an estate that costs more every month than the last, a field team whose reports are written from memory — and return systems that hold under use, under cost pressure and under audit.
Industry-agnostic by design. Technology-opinionated by habit: new tools earn their place by solving something the current ones cannot.
How we thinkNumbers we can actually stand behind.
Every other agency site puts client outcomes here. We do not have any we are permitted to publish, so these are facts about the work instead.
AI and automation, software, web, mobile, cloud, data, security, product.
Packaged routes we have built before, with the sequencing already decided.
Understand, decide, act, verify, learn — all five built before an agent writes.
Invent. Build. Scale.
Three words on our mark, and three genuinely different kinds of work. Most engagements are one of them — knowing which one you need is half the scoping.
What we build
Seven capabilities, one engineering standard. Most engagements start in one of these and end up touching two or three, because real problems do not respect service boundaries.
Systems that read, decide and act
Generative AI applied where it removes real work: retrieval over your own documents, agents that complete multi-step tasks, and automation that holds up under audit.
- Generative AI applications
- AI agents and tool use
- AI chatbots and assistants
- RAG and knowledge systems
- Document intelligence
Software built to be changed later
Custom applications and APIs designed around the seams that will move — so the second year of a system costs less than the first, not more.
- Web applications
- Mobile applications
- SaaS platforms
- Enterprise applications
- APIs and backend systems
Infrastructure that scales with the business
AWS architecture, migration and modernisation, with cost and operability treated as design inputs rather than things discovered on the invoice.
- AWS architecture
- Cloud migration
- Serverless and containers
- CI/CD
- Observability
Data your systems can actually use
Pipelines, warehouses and models that make reporting reliable and make the organisation ready for AI without a second migration.
- Data platforms
- Data pipelines
- Warehouse modelling
- Business intelligence
- Dashboards
Repetitive work, turned into a system
Process and workflow automation across the tools a business already runs — with exception handling designed in, because the exceptions are the work.
- Business process automation
- Workflow automation
- CRM and back-office automation
- API automation
- Internal tools
Fewer ways for it to go wrong
Application and cloud security, identity, monitoring and recovery — the work that decides whether a bad day is an inconvenience or an outage.
- Application security
- Cloud security posture
- Identity and access
- Monitoring and alerting
- Reliability engineering
From a thesis to a product that holds
Strategy, design and engineering under one team, so decisions about scope, interface and architecture are made in the same room.
- Product strategy
- UX and UI design
- Prototyping
- MVP development
- Product modernisation
AI is a pillar here, not a wrapper.
The useful part of applied AI is rarely the model. It is retrieval, tooling, evaluation and the integration work that decides whether an answer can be trusted and an action can be undone.
Data → Intelligence → Action → Outcome
Data
Documents, records and events inventoried, cleaned and permissioned. This is most of the timeline, and skipping it is why AI projects stall.
Intelligence
Retrieval and models applied to that content, with citations back to source and a threshold below which the system declines rather than improvises.
Action
Tools called inside an explicit permission boundary. Writes are staged or reversible, and anything sensitive passes an approval gate.
Outcome
Verified against schema and business rules, logged as a readable trace, and measured against an evaluation set so quality can be seen to move.
Every stage is a place to put a control. We build all four before widening what a system is permitted to do.
Named the way you would describe the problem.
Capabilities are how we organise engineering. Solutions are how the problem usually arrives — and two of these we have already built end to end.
Field sales tracking app
The van, the shop, the order — captured where there is no signal.
See the shape 02B2B client and dealer rewards
A loyalty programme your finance team can reconcile.
See the shape 03AI chatbots and assistants
Answer the questions your team keeps answering.
See the shape 04AI agents and intelligent workflow
Finish the task, not just describe it.
See the shape 05AWS cost optimization
Make the bill proportional to the work.
See the shape 06Workflow and process automation
Turn repetitive work into a system.
See the shape 07Custom software and platforms
Build the part that is actually yours.
See the shape 08Cloud modernisation
Move it without moving the problem.
See the shapeHow an engagement actually runs
Seven stages. Select one — the panel changes. The first two decide whether the other five are worth running at all, which is why we do them before quoting for the rest.
Invent, build, scale — in sixteen seconds.
A possibility cloud resolves into a structure, and the structure replicates. It is the same idea as the mark, and the same geometry as the canvases running elsewhere on this page.
Rendered by us, from the same code that draws the site. No stock footage, no office montage, no handshake.
What the three words meanSix problems, described the way they arrive.
Each one sets out the problem, the approach, what we would measure, and the thing most likely to go wrong. Read one before you call us.
Support questions answered before they become tickets
A retrieval assistant on the help centre and inside the product, grounded in your own articles.
Read the use case Document intelligenceInvoices and forms read into structured fields
Extraction with a confidence threshold and a human review queue for anything below it.
Read the use case AI agentsReconciliation across two systems that disagree
A bounded agent that fixes what it is permitted to fix and escalates the rest with a recommendation.
Read the use case Cloud & AWSA cloud bill that can be explained line by line
Attribution first, then waste, then rightsizing, then commitments — in that order.
Read the use case Field operationsField visits you can verify
Offline-first capture at the shop, so the record is made where the work happens.
Read the use case B2B loyaltyA channel scheme finance can reconcile
Points traceable to a settled transaction, with reversal rules decided before launch.
Read the use caseThe technology we actually work with.
Not a logo wall. This is the working set — what we choose from, and what we are prepared to run in production and hand over to your team.
Industry-agnostic, deliberately.
The engineering problems repeat across sectors; the domain rules do not. We learn yours rather than claiming we already have.
Financial Services
Systems where correctness, auditability and access control are the product, not a layer on top of it.
Healthcare
Sensitive data, strict access rules and integrations with systems that were not designed to be integrated with.
Retail
Inventory, pricing and fulfilment held consistent across channels that each believe they are the source of truth.
E-commerce
Catalogue, checkout and post-purchase operations, plus the automation that keeps support volume flat as orders grow.
Education
Content, cohorts, assessment and reporting — usually with a long tail of institutional process to model faithfully.
Manufacturing
Shop-floor data, planning systems and the offline-tolerant tools people actually use on the floor.
Logistics
Movement, exceptions and visibility. The value is almost always in how well the exceptions are handled.
Real Estate
Listings, documents and long transactions with many parties and a great deal of unstructured paperwork.
Work, described honestly.
JANNEX does not publish client names, logos, testimonials or outcome metrics. The projects below are our own demonstration builds and reference methods, described so you can judge the engineering rather than the marketing. Where a real engagement is relevant to your problem, we will discuss it directly with you under the confidentiality it deserves.
Knowledge assistant over private documents
A retrieval assistant that answers from an organisation's own policy and process documents, cites the paragraph it used, and reports every question it could not answer.
Read the build Demonstration buildBounded agent for back-office exceptions
An agent that resolves routine exceptions across two systems, verifies its own output against business rules, and escalates anything outside its permitted boundary.
Read the build Reference methodAWS cost and architecture review
The review method we run against an AWS estate: architecture read alongside the bill, waste attributed to an owner, and changes sequenced from zero-risk to structural.
Read the buildWhat our clients say
We would rather show you an empty space than a quote we made up.
We do not publish testimonials yet. Everything we have built so far has been under agreements that do not permit it, and a quote we cannot attribute is worth less than an honest blank space. What we can do instead is put you in a room with the engineers who did the work and walk you through the architecture, the code and the parts that went wrong.
Ask for a technical walkthroughThe first client who permits attribution goes here, with their name on it.
The second slot is for a named technical reference — someone you can call.
The third is reserved for a published outcome we can evidence with numbers.
Latest writing
What JANNEX is, in plain terms
What is JANNEX?
JANNEX is a technology company that builds intelligent products, software and systems. Work spans applied AI and automation, software and product engineering, cloud and AWS, data platforms, and security and reliability.
What does JANNEX build?
Custom software and platforms, web and mobile applications, APIs and integrations, AI assistants and agents, document and workflow automation, data pipelines and analytics, and the cloud infrastructure underneath them.
Who does JANNEX work with?
Startups building a first product, established companies modernising systems they already depend on, and enterprises adding engineering capacity or specialist AI and cloud work to an existing team. We work with organisations globally and remain industry-agnostic.
How does JANNEX work?
Seven stages: understand, define, design, build, launch, learn, scale. Scope and trade-offs are written down before engineering starts, delivery runs in short cycles against a working environment, and what we measure is agreed at the start rather than chosen after launch.
Does JANNEX provide AWS services?
Yes — AWS architecture and review, migration and modernisation, serverless and container platforms, infrastructure as code, observability, cloud security and AWS cost optimization.
Does JANNEX build AI chatbots and AI agents?
Yes. Chatbots are retrieval-based assistants grounded in your own knowledge, with citations and human handoff. Agents additionally take actions in systems, inside explicit permission boundaries with verification and full action traces.
Where is JANNEX based?
JANNEX operates from India and works with organisations internationally. Engagements run remotely by default, with on-site time where a project genuinely benefits from it.
Have something worth building?
Tell us the constraint you are working against. If we are not the right people for it, we will say so.
Or write to connect@jannex.in