[ DELIVERYDEVS AI ]
Deliverydevs AI Integrate AI where you need it
Four services, from working out where AI fits in your organization to building it into your systems and products. Every engagement starts with a data audit, so you only pay to build what your data can support.
Some problems need AI, Some need machine learning. Some need a report
Generative AI is the right tool when the work involves language: documents, emails, questions, conversations. When the work is prediction on your numbers, machine learning is usually cheaper and easier to explain. We tell you which one you need before you spend on either.
For organizations that want AI, but don't know where it fits
Most AI projects stall because they start with a tool instead of a problem. We start with your data and your operations, find the places where AI saves real time or money, and tell you plainly where it won’t. You leave with a plan you can act on, with us or without us.
Use cases
Deciding which department or process to start with
Choosing between building your own, buying a product, or using a model provider’s ready-made tool
Checking whether your data is ready before you pay for a build
Benefits
You spend first on the use cases with the clearest return
You avoid paying for pilots your data cannot support
Your team works from one plan instead of five vendor pitches
What you take home
Under the hood
Data profiling across source systems, use-case scoring on value and feasibility, and total cost of ownership comparison between hosted and self-hosted models.
Use cases
Staff asking policy, HR or procedure questions
Sales finding past proposals, pricing and contract terms
Support answering customers from your product documentation
Benefits
Less time searching, more time doing the work
Every answer shows its source, so it can be checked
Sensitive documents stay restricted to the right people
What you take home
For teams that spend hours finding answers that already exist
We connect a language model to your own documents and systems, so your team can ask a question in plain words and get an answer drawn from your files, with the source shown. It only reads what each person is allowed to see.
Under the hood
Retrieval-augmented generation over your document stores, vector search, permission filtering on retrieved content, and evaluation against a test set of your own questions. Works with Claude, ChatGPT and open models, chosen per use case.
For processes that move slower than they should, including software development
We automate repetitive multi-step work across your systems: approvals, data entry between tools, report preparation, and routine engineering tasks like code review and test writing. AI handles the steps that need judgment on text. People approve the steps that carry risk.
Use cases
Moving data between your ERP, CRM and email without retyping
Routing incoming requests to the right person based on what they say
Speeding up software delivery with automated code review, test generation and release notes
Benefits
Fewer manual handoffs and fewer data entry errors
Work keeps moving outside office hours
Developers spend more time building and less time on routine checks
What you take home
Under the hood
Event-driven orchestration across ERP and third-party APIs, language model steps with human approval gates, and retries, logging and alerting on every step.
[ What We Build ]
Four lines. One build standard
Deliverydevs AI is one of six practices, not a separate company. Same engineers, same spec, same handover.
[ 02 ]
LLM Integrations
Retrieval and permissions layers that let a model work against live business data.
[ 04 ]
AI Consulting and Strategy
funding, build versus licence,
and in what sequence.
[ The Constraint ]
Most AI projects fail before the model does
The hard part is the retrieval layer, the permissions model, and the approval chain, all of which have to exist before a model touches live business data. Without them, a model produces output somebody retypes. We build that layer first.
100+
CLIENTS DELIVERED
96%
CLIENT RETENTION
10 million+
RECORDS PROCESSED PER DAY
[ The Build Log ]
Two builds, documented
[ 01 ]
Pakistan Customs
Centralized Command and Control
THE BRIEF
Unify three vehicle-tracking feeds into one national view.
THE CONSTRAINT
Falcon-i, V-Track and NLCSS each exposed a different schema. None could be changed.
The architecture
A normalisation layer ahead of the command platform, with an AI predictive engine for risk and anomaly detection.
The decision
We normalised on read rather than on write. Writing to a shared schema would have required three vendors to ship in step, and none of them answered to us.
The output
282 active trips monitored in one interface.
[ 02 ]
IoT-Konnect
Fleet platform performance
THE BRIEF
The fleet platform was slowing under its own volume.
THE CONSTRAINT
10 million+ records a day, against queries written for a schema that assumed thousands.
The architecture
Query paths rebuilt, indexes restructured, the API surface reduced.
The decision
We rewrote the query layer instead of sharding. Sharding would have moved the problem and added an operations burden the team could not carry.
The output
10 million+ records processed per day.
[ How We Work ]
Spec it, Build it, Ship it
Five steps. The same five on every build, including the ones nobody sees.
01 · SPEC
We define exactly what is being built, for whom, and under what constraint, in writing, before anything else starts.
02 · BUILD
Engineers build against the spec. Scope changes get logged and re-quoted, not absorbed silently.
03 · REVIEW
04 · SHIP
The system goes live on the date set in step one. If it can’t, you hear that the week it becomes true, not the week it was due.
05 · MAINTAIN
[ The Standard ]
The standard is the brand
Four things that hold on every build. None of them is a promise.
Documented by default
Every scope, decision, and trade-off is written down as it happens, not reconstructed for a final report.
No padded timelines
beating them.
Senior engineers, not account managers
The people writing the architecture are the people you talk to.
Built to be maintained
Every system ships with the documentation the next engineer, ours or yours, will actually need.
[ Who Make Us Stronger ]
Who Make Us Stronger
[ Get In Touch With Us ]
Let’s Start a Project Together
Book a Free Consultation
[ FAQs ]
Frequently Asked Questions
Do you build models, or integrate existing ones?
What happens if the data is not ready?
We say so in the spec, before the build starts. Incomplete, unlabelled or inconsistent data produces output nobody trusts, and that failure surfaces after launch. Where the data needs work, it is scoped as its own line item.
Who owns the model and the pipeline after handoff?
You do. The code, the weights where we trained them, the pipeline definitions, and the decision log. Every system ships with the documentation the next engineer needs, ours or yours.
How do you price an AI build?
Scope sets the price. We quote against a written spec, so the number reflects the actual build rather than a package tier. Ask for line items and you get them.
What if the use case turns out not to be worth funding?
Then we say that, and the engagement ends there. Identifying which use cases are not worth building is most of what the advisory work is for. Cheaper than discovering it in month five.
Which model vendors do you work with?
Anthropic, OpenAI, and Google, plus open-weight models where the data cannot leave your infrastructure. The choice follows the constraint: data residency, latency, and cost per call, in that order.
