Production AI development: from demo to dependable.
A notebook that answers well once is not a product. We take LLM and agent applications the last mile: evaluation you can trust, guardrails against real attacks, observability into cost and quality, and serving infrastructure that holds up under load. We own the platform so you can swap the model.
For teams with a promising prototype and a launch date.
Production AI development earns its place when a model has proven it can do the job and the pressure has shifted to doing it reliably, safely and affordably. These are the situations where the demo-to-dependable gap is the thing standing between you and shipping, whether you are a fast-growing product company or an established business embedding AI into a live workflow.
A demo that impressed the room
The prototype wins meetings but breaks on real inputs. You need it to behave the same on the thousandth request as it did on the first.
No way to measure quality
Prompt and model changes ship on vibes. You want evaluation sets and regression suites so you can see quality move before customers do.
Security and compliance in the room
The app touches sensitive data or takes actions. Prompt injection, data leakage and tool misuse have to be handled before launch, not after.
Costs and latency out of control
Token spend is unpredictable and responses are slow under load. You need visibility, caching and sensible model choices per route.
An agent that needs guardrails
Your agent calls tools and APIs. You want permission boundaries, validation and fallbacks so autonomy never becomes a liability.
Serving it yourself
You are self-hosting open models for cost or data control and need GPU scheduling, autoscaling and a vector store that stays fast.
Engineer the system around the model, not just the prompt.
The model is the smallest part of a production AI system. Most of the risk lives in everything around it: how you know it works, how it fails safely, how you watch it, and how it scales. We treat the model as a replaceable component behind a platform we build and own, so you can move from one model or provider to another without rewriting the product. Every decision is grounded in evaluation and observability rather than opinion.
Principles we hold to on every production AI build.
- Evaluation comes first. Nothing tunes or ships without an offline eval set and a regression suite that catches quality drops early.
- Guardrails are defence in depth: input and output validation, prompt-injection defence, PII handling and tool permission boundaries, not a single filter.
- Everything is observable. Tracing spans, token, cost and latency metrics, and drift alerts are wired before launch, not after the first incident.
- Reliability is designed in with fallbacks, timeouts, retries, caching, rate limits and graceful degradation when a provider misbehaves.
- The platform is provider-agnostic. We own the serving, evaluation and safety layers so you can swap the model on cost, quality or latency.
What a production AI engagement covers.
Evaluation harness
Offline eval sets, task-specific metrics, groundedness and faithfulness checks for RAG, and calibrated LLM-as-judge scoring where it helps.
Regression suites
Every prompt, model or retrieval change runs through automated checks in CI, so quality changes are visible before they reach users.
Guardrails & safety
Input and output validation, prompt-injection defence, PII detection and redaction, content safety, and enforced tool and permission boundaries.
Observability
Tracing spans across retrieval, tools and generation, plus token, cost, latency, drift and quality alerts on live traffic.
Reliability layer
Fallback models and providers, timeouts, retries with backoff, response caching, rate limits and graceful degradation under load.
Serving & infrastructure
Inference autoscaling, GPU scheduling on Kubernetes, vector stores, and region choice for data residency, all as infrastructure-as-code.
Production AI development is where our AI agents work grows up into a dependable product. It sits on the same substrate we build for platform engineering and Kubernetes consulting, and we harden it with adversarial AI red teaming before launch.
Our production AI development process.
Four phases, planned backwards from a safe, measurable launch. The architecture review confirms exact scope, model strategy and success metrics before we build.
Baseline & evaluate
We define success metrics, build offline eval sets from real and adversarial inputs, and measure the current prototype honestly.
Harden & guardrail
Guardrails, validation, PII handling and tool boundaries go in, with regression suites wired into CI so quality is defended on every change.
Instrument & scale
Tracing, cost and latency monitoring, reliability layers and serving infrastructure make the app observable and ready for real load.
Red-team & launch
We red-team against the OWASP LLM Top 10, close the gaps, and ship behind drift and quality alerts. Everything lives in your repositories.
The tools we actually use.
No reseller agreements and no partner quota. Recommendations follow your workload, your data and your cost envelope. Typical building blocks on a production AI build:
Models & orchestration
Hosted model APIs or self-hosted open models behind a provider-agnostic layer, with retrieval and agent orchestration you can reason about.
Evaluation
Offline eval sets, regression suites in CI, groundedness and faithfulness scoring for RAG, and carefully calibrated LLM-as-judge.
Guardrails
Schema validation on inputs and outputs, prompt-injection defence, PII detection and redaction, and content safety on both sides of the model.
Observability
OpenTelemetry tracing spans, token, cost and latency metrics, and drift and quality alerts wired into your existing dashboards.
Serving on Kubernetes
Inference autoscaling, GPU scheduling that can scale to zero between bursts, and vector stores sized for recall and latency.
Security baseline
OWASP LLM Top 10 controls, tool and permission boundaries, secrets management, and pre-launch red-teaming of the whole system.
Production AI development FAQ.
Why do AI demos fail in production?
A demo proves the model can do something once, on curated inputs, with a forgiving audience. Production sees the long tail: adversarial prompts, malformed data, edge cases, concurrency and cost at scale. Failures show up as hallucinated answers, silent quality regressions when a prompt or model changes, runaway token spend, timeouts under load and prompt-injection exploits. The gap is rarely the model itself. It is the missing evaluation, guardrails, observability and reliability engineering around it, which is exactly what production AI development supplies.
How do you evaluate an LLM or agent app?
We build an evaluation harness before we tune anything. That means curated offline eval sets drawn from real and adversarial inputs, task-specific metrics, and for retrieval-augmented apps, groundedness and faithfulness checks so answers stay tied to sources. LLM-as-judge is used carefully, calibrated against human labels and never as the only signal. Every prompt, model or retrieval change runs through a regression suite so you can see quality move before it ships, not after a customer reports it.
How do you defend against prompt injection and data leakage?
We treat the OWASP LLM Top 10 as a baseline and design defence in depth. Inputs and outputs are validated, tool and permission boundaries are enforced so an agent can only reach what it should, untrusted content is isolated from instructions, and PII is detected, redacted or tokenised before it reaches a model or a log. Content safety filters sit on both sides of the model. Before launch we red-team the system against injection, exfiltration and jailbreak attempts and close the gaps we find.
How do you monitor an AI app in production?
We instrument the full request path with tracing spans so you can follow a single call through retrieval, tool use and generation. Token usage, cost and latency are tracked per request, route and model, so spend never surprises you. Quality is watched with sampled online evaluation and drift alerts, so a degradation in inputs or a regression after a change raises an alert rather than hiding. Dashboards and alerts are wired before launch, not retrofitted after the first incident.
Do we need GPUs, and where should the app run?
It depends on whether you call a hosted model API or self-host. Many production apps run fine against managed model endpoints and need no GPUs at all. When you self-host open models for cost, latency or data-control reasons, we run inference on Kubernetes with GPU scheduling, autoscaling that can scale to zero between bursts, and right-sized nodes so you are not paying for idle accelerators. Where the app runs is driven by your data-residency needs, met generically through region choice, and by cost and latency trade-offs rather than a fixed answer.
Ready to take your AI app to production?
Start with the readiness scorecard, or book a free 30-minute architecture call. A senior engineer reviews your app, model strategy and risks, and returns a concrete plan to get it dependable.