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PRANINNOVATIONSProduction Specialists

Core practice

AI development that ends in a system your team operates

One team from the first workshop to the day your staff stop asking us questions. Agents, retrieval over your own documents, custom models, and the ordinary software that has to exist around them.

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Production architecture for the End-to-End AI Development practice

End-to-End AI Development

The situation

Before we start

01

The pilot cleared the committee, then stopped

Nobody scoped integration, access control, or who owns it on Monday. We scope those in week one, before anyone writes code.

02

Nobody agreed what working means

Projects drift when success is a feeling. Accuracy, latency, throughput and uptime are written as figures before build starts.

03

The people who designed it never built it

Specifications lose their reasoning in transit. The engineers who scope your system are the ones who build and commission it.

What working means here

Signed first. Measured after.

The value of each one is set with you during scoping, from your data and your line. What does not change is that they are written down first and published against afterwards, pass or fail.

  1. 01

    Accuracy against threshold

    On a held-out set your team helped assemble, published pass or fail

  2. 02

    Availability

    Measured in your environment over the acceptance period, not quoted from a datasheet

  3. 03

    Time to handover

    From signed scope to your team operating it without us on the call

We publish the measurements behind this practice, including the runs that failed.

RAG Scale & Cost Benchmark 2026

How it is assembled

Stage by stage

Each one ends in something you keep.

  1. Step 01

    Discovery workshop

    Two days with the people who do the work today. We leave with the problem, the data reality, and the numbers that define success.

  2. Step 02

    Architecture and scope

    A written architecture, an integration plan, and a phased quote. Scope freezes before build starts.

  3. Step 03

    Build in phases

    Each phase ends in something you can use, on your infrastructure, reviewed with your team.

  4. Step 04

    Evaluation and acceptance

    Tested against the criteria agreed in phase one. The result is published whether it passes or not.

  5. Step 05

    Enablement and handover

    Runbooks, training and IP transfer, or we stay on under an operations agreement.

Scope

What the work includes

  • Discovery: the operational problem, the data that exists, and what good looks like as a number
  • Architecture: where it runs, who reaches it, how it fails safely
  • Build: models, services, integrations and the interfaces staff use daily
  • Evaluation: a held-out set, an agreed threshold, and a signed record of the result
  • Deployment into your accounts, your region, under your identity provider
  • Enablement: runbooks, administrator training, a named escalation path
  • Weekly written reporting on scope, progress and risk
  • Assignment of source, weights and documentation on completion

Deliverables

What you keep

  1. 01Signed scope with numeric acceptance criteria
  2. 02Architecture and access-control design
  3. 03Working system in your environment
  4. 04Evaluation report against the agreed thresholds
  5. 05Runbooks and administrator training
  6. 06Source, weights and IP assignment

Typical stack

Python · TypeScript · PyTorch · PostgreSQL · Kubernetes · Terraform · AWS / Azure / GCP

Manufacturing · Vision

Surface defect inspection, decided at the camera in 12 ms

Final inspection was manual and sampled, so surface defects were being found by the customer rather than on the floor. We surveyed the cell, captured production across three shifts, and commissioned an edge vision station that inspects every part and drives the existing reject gate.

Read the engagement
12 ms
decision latency at the camera
99.4%
detection rate on hold-out set
100%
of parts inspected, up from sampled

Start

How End-to-End AI Development begins

Production builds $75K–180K · Enterprise systems $180K–500K+

Where an engagement lands inside its band is set by data readiness, integration depth, the reliability bar, evaluation burden, deployment constraints and usage economics. All six, explained.

Thirty minutes on the problem and where it happens, then a fixed-price pilot if it looks like a fit.

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FAQ

Asked before signing

Production builds run eight to fourteen weeks across three or four phases. Enterprise systems run three to six months or longer. Discovery and architecture take three to four weeks of that.

Yes. Your cloud accounts or your facility, under your identity provider and your access controls. We do not run production systems on infrastructure you cannot audit.

You do. Source, trained weights, documentation and infrastructure definitions, assigned in the contract rather than negotiated at the end.

Often. We start with a two-week assessment of what exists and give you a written recommendation to continue, refactor or restart, with the cost of each.

Often paired with

Where this fits with the rest

  1. 01Core AI

    AI Product Development

    AI-native products, and AI features inside products that already have customers.

  2. 02Start here

    AI Pilot

    Four weeks, fixed price, signed acceptance criteria. Working software, or a written case for stopping.

  3. 03Physical AI

    Edge AI & Computer Vision

    Detection, inspection, tracking and safety on-device. Milliseconds, and no network dependency.