What are custom AI applications?
A custom AI application puts a language model, or another AI component, to work on a task specific to your business. Examples include an assistant that answers staff questions from policies and contracts, a pipeline that reads incoming documents and fills your systems, a semantic search over technical knowledge, or a copilot embedded in an internal tool.
The model is a component, not the product. Most of the work lies around it: finding and preparing the right context, connecting to your systems, controlling what the application may do, measuring its answers and keeping cost and latency acceptable. That engineering separates a demo that impresses in a meeting from an application people rely on every day.
The scope covers retrieval-augmented generation (RAG), structured extraction from documents, recommendation and semantic search, and agents that plan and call tools. Each is built with evaluation and guardrails from the first version. These applications belong to our AI consulting practice, and often follow an AI strategy assessment that ranked the use case first.
Signs you need a custom AI application
Off-the-shelf assistants cover general tasks well. A custom application becomes worth its cost when the task depends on your own data, your own systems or your own rules. These signs point in that direction.
A chatbot demo works, but nobody dares launch it
The prototype answers well in front of the team, yet nobody can say how often it is wrong or what it might tell a customer.
Staff search many systems for the same answers
Knowledge sits in wikis, tickets, contracts and shared drives, and experts spend their days answering the same questions.
Documents are still read and keyed in by hand
Invoices, claims, forms or contracts arrive in varied formats, and people copy the data into your systems.
Generic tools cannot touch your data or systems
Security rules, data residency or the need to act inside internal applications rule out consumer assistants.
AI cost or latency is growing without control
An existing application calls a large model for every request, and the bill or the response time is no longer acceptable.
The first sign is the most frequent. Building a convincing demo on a language model takes days; knowing how good it really is takes an evaluation set, and that is the step most projects skip. We start there, before writing any application code.
What our custom AI engagement covers
The deliverables cover the application itself and everything needed to trust it, change it and run it after we leave. Nothing depends on a proprietary platform of ours, so the code and the knowledge stay with you.
Use case and evaluation set
The task defined with real examples and expected answers, so quality is measured from the first prototype rather than judged by impression.
Retrieval and data pipeline
Documents cleaned, chunked, indexed and kept current, with permissions respected so users only see what they are allowed to see.
Application and integration
The model, prompts and tools wired into your systems through APIs, with every request and response logged.
Guardrails and access control
Input and output checks, limits on the tools an agent may call, human approval for sensitive actions and defenses against prompt injection.
Monitoring, cost control and handover
Quality, latency and cost per request tracked in production, with a documented process to update prompts, models and data.
The evaluation set is the asset that lasts longest. Models and prices change often, and with a good set of test cases you can move to a newer or cheaper model in days, because you can show that the answers did not get worse.
Demo working, launch blocked?
A discovery phase typically takes 2 to 4 weeks and ends with an evaluation set, an architecture and a delivery plan.
How we build AI applications: four phases
The method is iterative, but each phase has an exit condition based on measured quality, not on how good the demo looks. Every change after the prototype must earn its place on the evaluation set.
PHASE 01
Define
Task, users, data sources, risks and evaluation set.
PHASE 02
Prototype
Simplest working version, scored against the evaluation set.
PHASE 03
Harden
Guardrails, permissions, integration, load and adversarial tests.
PHASE 04
Operate
Production monitoring, feedback capture and controlled updates.
The prototype is deliberately simple: one model, basic retrieval, no agent unless the task needs one. Its score shows where the errors come from, whether missing context, poor retrieval or the model itself, and each later change is kept only if the score improves. User feedback from production is added to the evaluation set over time.
RAG, fine-tuning or an agent?
These patterns are often confused, and choosing the wrong one is the most expensive early mistake in an AI application project. The table shows what each one is for and what it costs to run well.
| Pattern | What it does | When it fits | Main cost |
|---|---|---|---|
| Prompting alone | Instructions and examples sent with each request | General tasks needing no private knowledge | Limited to what the model already knows |
| Retrieval (RAG) | Finds relevant passages and gives them to the model | Answers must come from your documents, with sources | Indexing, permissions and retrieval quality |
| Fine-tuning | Adjusts a model on your own examples | A consistent format or style at high volume | Training data, retraining and evaluation |
| Agent with tools | Plans steps and calls APIs to complete a task | Multi-step work across several systems | Harder to test, needs strict guardrails |
| Classic ML model | Predicts a label or value from structured data | Narrow, high-volume decisions on tabular data | Labeled history and monitoring |
Most business assistants need retrieval, not fine-tuning, because the knowledge changes and answers must cite their sources. Agents are worth their complexity only when a task really requires several steps across systems; for a single lookup, a fixed workflow is easier to test and safer to run.
Risks and guardrails for AI applications
Language-model applications fail in ways traditional software does not: they can be confidently wrong, manipulated through their inputs or quietly expensive. We address each risk in the design, not in a disclaimer at the bottom of the screen.
Answers that sound right and are wrong
We measure factual accuracy on the evaluation set, require sources for retrieved answers and let the application say it does not know.
Prompt injection and data leakage
Text in documents or user input can try to override instructions. We isolate untrusted content, restrict tools and filter outputs.
Agents taking unintended actions
Every tool an agent can call has explicit limits, and actions that change data or spend money need human approval.
Uncontrolled cost and latency
Caching, smaller models for simple steps and limits per user keep the cost of each request predictable.
Silent quality regressions
A model update or a prompt change can break cases that used to work. The evaluation set runs before every release.
Personal and confidential data get the same attention. We document which data reaches which model, where it is processed and how long it is kept, in line with GDPR and your sector rules. When data must stay in your environment, the architecture uses models hosted there.
Custom AI application pricing
Every engagement starts with a diagnosis and ends with a fixed quote. Durations are typical, not promised.
| Format | Duration | Price |
|---|---|---|
| Discovery and evaluation set | 2 to 4 weeks | Fixed quote after diagnosis |
| Application build and integration | 3 to 9 months | Fixed quote after the assessment |
| Monitoring and model updates | Monthly | Monthly fee quoted at handover |
What changes the price: number and quality of data sources, depth of integration with your systems, agent autonomy and guardrail requirements, and expected usage volume.
The quote covers senior consultants only, a defined scope and the deliverables of each phase, including the evaluation set, the code and the runbooks. Model usage fees, hosting and vector database costs are billed by their providers and estimated per request during discovery.
Custom AI applications FAQ
Which language model do you use?
The one that fits the task, measured on your evaluation set. We compare hosted and open-weight models on quality, cost, latency and data constraints, and design the application so the model can be replaced later.
Can the application use our confidential documents safely?
Yes, with the right design. Retrieval respects your existing permissions, data stays in your environment when required, and we document what reaches the model and what is logged.
How do you stop the assistant from making things up?
By grounding answers in retrieved sources, asking the model to cite them, allowing it to decline when the context is missing, and measuring accuracy on the evaluation set before every release.
When is an agent the right choice?
When the task needs several steps across systems and those steps cannot be fixed in advance. For predictable workflows, a scripted sequence with a model at specific steps is easier to test and safer.
How soon is a first version usable?
A prototype scored on the evaluation set comes early in the build. Production readiness depends on integration and guardrail requirements, and a full build typically takes 3 to 9 months.
Can our team maintain the application after handover?
Yes. The code, prompts, evaluation set and runbooks are handed over, and your engineers are trained to update data, change models and read the monitoring before we step back.