Generative AI development,
engineered to ship
Senior engineers build LLM applications grounded in your data — RAG, fine-tuning when it earns its place, and evaluation that gates every release into production.
This is the commercial build page in our AI Development practice. If your problem is less about generation and more about a system that decides and acts, see AI Agent Development. If the model has to live inside software you already run, see AI Integration Services. And if you're sizing the spend, here's what drives AI development cost.
Proof, measured
in production
Nine years shipping production software, now on an AI-native loop. Generative AI features that clear a real accuracy bar and keep running after launch.
100+
Web, SaaS, and AI features in production across fintech and healthcare, built to ship and hold up under load.
3–4wks
From a brief to a grounded LLM app over your data.
100%
Senior engineers on the model layer; no juniors learning on your data.
99.9%
Across the AI features and products we run after launch.
Ship a generative AI feature that holds up in production
Senior engineers and an eval-driven loop, from prototype to launch.
A generative AI app
that holds up live
A model behaves in a demo and surprises you in production. These six things are what close that gap, and they are in scope from the first sprint.
An evaluated app
Quality measured against a scored test set, so you launch on numbers you trust — not a handful of prompts that happened to work in the call.
Grounded in your data
Retrieval over your documents and records, so answers cite your sources instead of whatever the base model half-remembers from training.
Fine-tuning when it pays
We fine-tune only when clean data and a narrow task earn it. When RAG already does the job, we say so and skip the bill.
Guardrails and PII
Input and output checks, prompt-injection defense, and PII handling sized to your compliance posture. We design these in at the first sprint rather than bolting them on after an incident.
Cost and latency tuned
Model choice, caching, and prompt size tuned to unit economics, so the feature stays viable at real volume and not only in a pilot.
Tracing in production
Every request traced, with accuracy drift, cost, and latency on a dashboard — so a regression shows up before your users report it.
What we build with
generative AI
Generative AI is more than a chatbot. These are the build types we ship most — each grounded in your data and evaluated before it launches.
LLM apps & copilots
Chat, assistants, and copilots that live inside your product — the model reads a user's context, calls your API for the facts, and writes back a usable answer in one turn.
RAG & knowledge retrieval
A retrieval layer over your docs, tickets, and catalog — it looks up the current fact and answers from that, so a policy change or a new price shows up in answers the day it lands in your systems.
Fine-tuned models
Format, tone, or task behavior the stock model keeps getting wrong — corrected with a fine-tune when you have clean labeled examples and a job narrow enough for them to fix.
Document & content automation
High-volume drafting, summarizing, and rewriting: release notes, reports, replies, product listings, generated to a template and checked before anything reaches a customer.
Extraction & structured output
Turning messy documents, emails, and tickets into clean structured data — fields, labels, and JSON your systems can act on, scored against a labeled set for accuracy.
Agentic features
Generation that has to act: call a tool, check the result, decide what comes next. We build the agent loop, or pass it to our agent practice when that is the whole product.
From use case
to production AI
Six stages in three phases. Senior engineers and AI tooling move a use case from a scoped problem to an evaluated, guardrailed app running in production.
Scope
Frame & ground
Use-case scoping
We pin down the one task worth automating, the accuracy bar that makes it useful, and how we will measure it. Done wrong here, no amount of model work saves it.
Data & retrieval
Chunking, embeddings, and a vector store over your sources — tuned with retrieval evals so the model answers from your data, not from memory.
Build
Make & measure
Model build
Prompt design, structured output, and fine-tuning where the data earns it — wired into your APIs as an application, not a script that runs once.
Evaluation in CI
Scored eval sets run on every change. A pull request that drops accuracy fails the build, the same way a broken unit test would.
Ship
Guard & operate
Guardrails & safety
Output validation, PII redaction, and prompt-injection defense, with a person in the loop wherever a wrong answer is expensive to undo.
Integration & ops
Ship behind feature flags with tracing and cost monitoring, then watch the real traffic and tighten the prompts and retrieval against it.
Generative AI we
shipped with partners
From document AI on a blockchain ledger to a clinical platform and a crypto-finance ecosystem — LLM features that had to clear a real accuracy bar in production.
eIDAS-qualified e-signature platform with KYC & blockchain audit
One platform to sign agreements, verify identities, and collect payments, all eIDAS-qualified with a blockchain audit trail on every signature.
Medication management software for patients and care teams
Medication management software with two sides, an accessible patient app and a clinical adherence dashboard, joined by bidirectional HL7/FHIR EMR sync.
Crypto trading platform with wallet, card & academy
An integrated crypto trading platform that puts trading, hardware-wallet custody, a debit card, fiat on- and off-ramps, and an education academy under one account.
Field notes on
shipping generative AI
How we ground models in real data, write evals that catch regressions, and decide RAG against fine-tuning — from the engineers doing the work.
- AI & Machine Learning · 20 min · January 7, 2026
Model Context Protocol (MCP) for Developers
- AI & Machine Learning · 19 min · January 5, 2026
Claude Code Skills: Teaching Your AI Coding Agent Your Stack
- AI & Machine Learning · 18 min · January 2, 2026
The AI Software Engineer: What the Role Actually Is Now
- AI & Machine Learning · 18 min · December 31, 2025
AI Pair Programming: How Senior Engineers Work With AI
- AI & Machine Learning · 18 min · December 29, 2025
AI Coding Tools in 2026: The Landscape, Honestly
Generative AI
development questions
What founders and product teams ask before starting a generative AI build.
Generative AI development is building software where a language model does real work — drafting, answering, classifying, or extracting — grounded in your own data through retrieval, and sometimes fine-tuned when the data earns it. The output is a production application with evaluation, guardrails, and monitoring around the model, not a notebook demo that works once in a meeting and falls over on the second prompt.
Cost scales with scope and how deep the integration runs, not with a fixed price list. A grounded LLM prototype over your data is a few weeks of senior engineering; a production system with evals, guardrails, and ops on top of existing infrastructure is more. We run fixed-scope engagements and size the work to the question you actually need answered, so spend tracks delivered software rather than an open-ended retainer.
A working prototype — an LLM app with retrieval over your data that you can put real prompts through — usually takes 3 to 4 weeks. Getting to production depends on how much it has to integrate with and how strict the evaluation bar is. Hooking into one clean data source is fast; wiring into several legacy systems with a high accuracy threshold takes longer, and the eval work is what sets the real timeline.
We pick the model per engagement. Anthropic Claude and OpenAI GPT cover most frontier work; open-source models like Llama and Mistral come in when control, cost, or on-prem deployment matter. Retrieval runs on Pinecone, Weaviate, or pgvector depending on scale and what you already operate. Every build ships with an eval harness and request tracing so quality and cost stay measurable after launch.
Most teams need retrieval, not fine-tuning. RAG is the right call when the model needs to answer from facts that change — your docs, tickets, catalog, records — because you update the data without retraining anything. Fine-tuning earns its place when you need a specific format, tone, or task behavior the base model handles poorly, and you have clean labeled examples to teach it. We make that call honestly up front and tell you when fine-tuning would be spend with no payoff.
Yes — that is most of what the work is. We take a prototype and add the evaluation sets that gate releases, the guardrails for PII and bad output, the request tracing and cost monitoring, and the deployment and rollout plan. The same senior engineers who built the prototype carry it to production, so nothing gets thrown over a wall to a team that has to relearn the system from scratch.
Ship a generative
AI app that lasts
Partner with Idealogic for production generative AI — LLM apps, RAG, and fine-tuning, evaluated and guardrailed by senior engineers from prototype to launch.