AI Strategy and AutomationProposed offering

Take your AI pilot from a promising demo to a service you can run and trust.

We harden pilots with evaluation suites, production architecture, security, monitoring and operational ownership, so quality holds with real users, real volume and real cost.

Review your pilot's readiness See the production path

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Production path

Pilot
Evaluation suite
Hardening
Release pipeline
Monitoring
Operations

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

AI demos failing with real users

The pilot answered fifty hand picked questions beautifully. In production, users ask misspelt, vague and multi part questions, paste long documents and ask about things outside scope, and quality drops sharply.

Why it happens: Demos use friendly inputs. Real traffic is broader, messier and contains deliberate misuse.
What it costs: Loss of trust at launch that is hard to rebuild, and urgent patching under pressure.
How we approach it: We build an evaluation set from real and adversarial inputs before launch, define release thresholds, and add input handling, scope control and graceful fallbacks.
What to measure: Accuracy on a representative evaluation set, out of scope handling rate and user satisfaction after launch.

Inconsistent model behaviour

The same question gets a good answer on Monday and a poor one on Wednesday. A provider updated a model, a prompt was edited, and nobody knows which change caused the drop.

Why it happens: Models, prompts, retrieval settings and data all change, and there is no regression test or version record.
What it costs: Unpredictable quality and slow, argumentative debugging.
How we approach it: Version prompts, models, indexes and configuration, run the regression evaluation on every change, and pin model versions where providers allow it.
What to measure: Regression pass rate per release, time to identify the cause of a quality drop, and number of unreviewed changes in production.

Production cost escalation

The pilot cost a few hundred dollars. At production volume with larger documents and retries, the monthly bill is many times the business case.

Why it happens: Pilots do not exercise worst case token use, retry storms or inefficient retrieval.
What it costs: The project fails its own business case despite working technically.
How we approach it: Load test with realistic traffic, apply context limits, caching, model routing and batching, and set budgets and alerts.
What to measure: Cost per request and per successful task, projected monthly cost against budget, and cache hit rate.

Missing operational ownership

The innovation team built the pilot and moved on. Operations will not take it because there is no runbook, no alerting and no one on call.

Why it happens: Pilots are staffed as experiments, not as services.
What it costs: Production launch is delayed or the system runs unmonitored until a failure.
How we approach it: Define service ownership, service levels, alerts, incident procedures and a support model before launch, and train the operating team.
What to measure: Mean time to detect and resolve incidents, alert quality, and percentage of services with a named owner and runbook.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Evaluation framework

Labelled datasets, automated scoring, human review sampling and release thresholds, including groundedness and safety checks.

Customer need: Measure quality objectively and repeatedly.
Integration: CI pipeline and evaluation tooling.
Deliverable: Evaluation suite and report template.
Business value: Evidence behind every release decision.

Production architecture

Stateless services, queues, caching, rate limiting, timeouts, fallbacks and multi region options where required.

Customer need: Scale and fail safely.
Integration: Your cloud environment and networking.
Deliverable: Architecture and infrastructure as code.
Business value: Reliability under load.

Deployment pipelines

CI/CD with automated tests, evaluation gates, staged rollout and rollback of code, prompts and configuration.

Customer need: Release changes safely.
Integration: GitHub Actions, GitLab CI, Azure DevOps or Jenkins.
Deliverable: Pipelines and release process.
Business value: Frequent, low risk releases.

Monitoring and alerting

Traces, latency, error and cost metrics, quality sampling and drift alerts.

Customer need: See problems before users report them.
Integration: OpenTelemetry and your monitoring stack.
Deliverable: Dashboards, alerts and on call guide.
Business value: Fast detection and diagnosis.

Security hardening

Access control, secrets management, data redaction, prompt injection testing and abuse controls, informed by OWASP LLM risks.

Customer need: Protect data and block misuse.
Integration: Identity, key management and logging.
Deliverable: Security test results and control documentation.
Business value: Lower risk of leakage and misuse.

Operations and support model

Ownership, service levels, runbooks, incident handling and a continuous improvement cycle.

Customer need: Make the service supportable.
Integration: Ticketing and on call tools.
Deliverable: Runbooks and support plan.
Business value: Continuity after launch.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Review the pilot

Assess what the pilot proves and what it does not: scope, data, architecture, quality evidence and known failures.

Build the evaluation set

Collect real and adversarial inputs, label expected behaviour and set release thresholds with the business owner.

Select and fix models

Re-test candidate models on the evaluation set for quality, latency and cost, and pin the versions in use.

Harden the architecture

Add queues, caching, timeouts, fallbacks, rate limits and retries. Design for failure of the model provider.

Secure and govern

Apply access control, redaction and logging, run prompt injection and data leakage tests, and document data handling.

Automate release

Build pipelines that run tests and evaluations, deploy gradually and roll back on failure.

Launch with monitoring

Release to a limited cohort with dashboards and alerts, then expand as metrics hold.

Operate and improve

Review sampled outputs and incidents, add failures to the regression set, and track cost and quality trends.

Solution in action: Hardening a pilot assistant before a company wide launch

Reference Architecture An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

A policy question assistant worked in a pilot with one team. The company wants to launch to all staff next quarter.

Existing workflow

The pilot runs from a notebook backed service with manual restarts, no regression tests and unknown cost at scale.

Improved workflow

The service moves to a containerised deployment with queues and caching. An evaluation set of real questions gates each release. Dashboards track latency, cost and sampled answer quality. Alerts and a runbook make support possible.

The pilot is approved for company wide launch.

Systems involved

Identity provider, document store, search index, model provider, monitoring, CI/CD.

Data movement

Evaluation set, sampled production traces under a retention policy, cost metrics.

Human decisions

Policy owners review a weekly sample of answers. An on call engineer handles incidents.

Automation opportunities

Evaluation on every change, rollout gates, alerting and rollback.

Exception handling

When confidence is low or the model provider fails, users see a safe message and a link to the source documents.

Resulting user experience

Staff get consistent answers, and the owner can see quality and cost over time.

KPIs to evaluate

Evaluation pass rate
P95 latency
Cost per question
Incident mean time to resolve

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

Evaluation datasets, scoring code and release thresholds
Production architecture and infrastructure as code
CI/CD pipelines with evaluation gates and rollback
Monitoring dashboards, alerts and on call guide
Security test report and control documentation
Cost model, budgets and alerts
Runbooks and incident procedure
Handover and training for the operating team

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Delivery (proposed implementation options)

Docker
Kubernetes
Terraform
GitHub Actions
GitLab CI
Azure DevOps

Cloud

AWS
Azure
Google Cloud

Observability and evaluation

OpenTelemetry
LangSmith
Arize
Galileo
Grafana
Datadog

Security references

OWASP Top 10 for LLM Applications
NIST AI Risk Management Framework

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status

Reference ArchitectureInteractive demo with simulated data

Evaluation gated release pipeline for AI services

The quality engineering demonstration shows release readiness gates combining test results and AI evaluations, with simulated data.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Evaluation pass rate

Share of evaluation cases meeting thresholds at release.

Reliability

Availability and error rate against the service level.

Unit cost

Cost per successful task against the business case.

Incident response

Time to detect and resolve incidents.

Questions buyers ask

What makes a pilot production ready?

A measured quality level on a realistic evaluation set, a secure and scalable architecture, monitoring, a release process, a cost model and a named owner. We assess your pilot against each.

Can you work on a pilot someone else built?

Yes. We start with a code and architecture review, document risks and decide with you whether to harden, refactor or rebuild parts of it.

How long does hardening take?

Often one to three months, depending on the size of the pilot, the number of integrations and the evaluation work needed. We provide a plan after the review.

How do you prevent quality regression?

By versioning everything that affects behaviour and running the evaluation set automatically before each release, with thresholds that block deployment on failure.

Who runs it after launch?

You can run it, we can run it, or both. We prepare runbooks and train your team either way, and offer ongoing support under the maintenance service.

How do you handle model provider outages?

Through timeouts, retries, fallback providers or cached responses where appropriate, and clear user messaging. The design is tested by simulating failures.

Related services

Industries where this applies

Review your AI pilot's production readiness.

Share the pilot, its results and the launch date. We will list what stands between it and a supportable service.

Request a readiness review Explore a demonstration