FreshField.ai · Runtime Intelligence

Give AI the production view it is missing.

Runtime Intelligence gives AI the production view it is missing. It watches running systems, predicts and prevents failures and attacks, and hands engineers (or their AI) a ready fix with the evidence behind it.

See how it works

FreshField.ai: Runtime Intelligence

The problem

On paper vs in production.

AI writes much of our software, but it only ever sees code on paper. Code that passes locally and in lower environments still breaks in production.

Incidents are handled after the damage is done, by people reading gigabytes of logs.

On paper✓ Tests pass. Review approved.
1public Order checkout(Cart cart) {
2  var stock = inventory.reserve(cart);
3  var charge = payments.charge(cart.total());
4  return orders.save(cart, stock, charge);
5}
In production○ deploying
Timeout after 30 sPool exhaustedRetry storm

Break it yourself

Pick what goes wrong in production.

These eight causes only show up in running systems. Choose one to see the failure, then what Runtime Intelligence catches and the fix it would raise.

Real data is bigger and stranger than test data.
Illustration
  1. What breaks

    A customer with an unusually large order makes a query scan far more rows than any test did.

  2. Signal it saw

    sig-1188 · query time far above normal for orders-db

  3. Triage decision

    Real · actionable · high severity · owner: orders team

  4. Fix PR it would raise

    Add a paginated query and an index migration, with a regression test using a large order.

From noise to signal

Gigabytes of logs are not a job for a person at 3 am.

Drag the slider to see what Runtime Intelligence does before any AI looks at the data.

orders-service · stdoutIllustration

Raw logs: Every line, as it arrives. Dense and unreadable.

What it is

The round-the-clock engineer.

A platform that does, around the clock, every step a reliability engineer or proactive security team does, both to prevent incidents and to resolve them.

How it works

Seven stages, ending in a pull request a person merges.

  1. Logs, metrics, traces, deploy events, config and flag changes, infrastructure and security events, and SBOM data. Applications emit signal IDs, not function names. First languages: Java, C# and Python.

Stage 1 of 7: Collect

Illustration

Follow one log line

Pick a line and watch it travel.

Each sample contains a fake email address and a fake token. Press Run, or step through it yourself.

2026-03-14T03:02:11.482Z ERROR [orders-service] Payment call failed for user jane.doe@example.com token=tk_live_9fA27xQ81mZ timeout=30000ms orderId=88412

The raw line, as the Java service wrote it. The highlighted email and token are fake.

Illustration

Signals

Code details never leave the app.

Applications emit compact signal IDs created at build time, not function names. A versioned mapping file per release, like a source map, lives only on the platform. Hover, tap or focus a signal to decode it.

Illustrative. The real mapping lives only on the platform, never in the application or its logs.

The incident evidence pack

Everything an engineer needs, in one place.

Every escalated incident comes with an evidence pack. Tap a card to bring it to the front.

  • One timeline of logs, metrics, traces, deploys, config and flag changes.

    • 02:41 deploy orders-service
    • 02:58 flag checkout-v2 on
    • 03:02 payments latency rising
    • 03:04 error rate climbing

  • The signals that matter, linked to file, function and commit.

  • Recent commits ranked by how well they explain the signals.

  • Steps that reproduce the issue in a sandbox.

  • A ready-to-paste prompt for the engineer’s own AI assistant.

Illustrative example

3 am, before and after

The same incident, two ways.

Drag the divider, tap either side, or use the arrow keys.

Evidence pack
  • ✓ timeline
  • ✓ key signals
  • ✓ suspect commits
  • ✓ repro steps
Root cause: payment timeout lowered in e5179dc; connections not released on the error path.
Fix PR: release connection on error, capped retry, regression test
Waiting for human review
dashboardlogs (1)logs (2)tracesdeploysrunbookchatstatus page
03:00:10 ERROR checkout timeout after 30000ms
03:01:21 WARN pool wait 212 active 50
03:02:32 ERROR payments 504 upstream
03:03:43 INFO GET /healthz 200
03:04:14 ERROR checkout timeout after 30000ms
03:05:25 WARN pool wait 212 active 50
03:06:30 ERROR payments 504 upstream
03:07:41 INFO GET /healthz 200
03:08:12 ERROR checkout timeout after 30000ms
03:09:23 WARN pool wait 212 active 50
03:00:34 ERROR payments 504 upstream
03:01:45 INFO GET /healthz 200
03:02:10 ERROR checkout timeout after 30000ms
03:03:21 WARN pool wait 212 active 50
03:04:32 ERROR payments 504 upstream
03:05:43 INFO GET /healthz 200
03:06:14 ERROR checkout timeout after 30000ms
03:07:25 WARN pool wait 212 active 50
03:08:30 ERROR payments 504 upstream
03:09:41 INFO GET /healthz 200
03:00:12 ERROR checkout timeout after 30000ms
03:01:23 WARN pool wait 212 active 50
03:02:34 ERROR payments 504 upstream
03:03:45 INFO GET /healthz 200
03:04:10 ERROR checkout timeout after 30000ms
03:05:21 WARN pool wait 212 active 50
03:06:32 ERROR payments 504 upstream
03:07:43 INFO GET /healthz 200
● PAGER · checkout error rate high
Acknowledge · 3:04 am
BeforeAfter
Illustration

Reliability intelligence

Prevent, detect, resolve.

Prevent

  • Pre-deploy risk checks
  • Capacity forecasts
  • PR comments before merge, such as “this code path times out under production load”

Detect

  • Anomaly detection
  • SLO burn-rate alerts

Resolve

  • Root cause with evidence
  • Approved runbook actions
  • Fix PRs with a sandbox reproduction and a regression test

Security intelligence

Find the paths an attacker would take, then close them.

Attack scenarios are drawn as attack-path graphs mapped to MITRE ATT&CK, reported, then fixed with a PR per finding. They run only in a sandbox.

Sandbox

Tap a node to see what it is and which capability found the risk.

Illustration

Everything visual

See it all, not just read it.

Concept mockups of the product screens. Values shown are illustrative.

Product concepts
  • Service map

    Service map

  • Incident timeline
    deployflaglatencyerrorsfix PR

    Incident timeline

  • Signal-to-code explorer
    sig-3302→Charge.csChargeAsync()
    sig-0915→Pool.javarelease()
    sig-1188→repository.pyfind_by_customer()

    Signal-to-code explorer

  • Attack-path graph

    Attack-path graph

  • SLO and error budget board
    checkouterror budget
    orderserror budget
    searcherror budget

    SLO and error budget board

  • AI action log
    Draft PRrelease connection on errorpending
    Runbookrotate certificateapproved
    Commenttimeout risk on PRposted

    AI action log

Also: Ask in plain language · PDF reports for management and auditors.

Ask in plain language

Ask your production a question.

Concept preview. Answers come from preset examples with illustrative values.

Safety and trust

It proposes. People decide.

Autonomy is set per service, up to a ceiling set by the licence. Every AI action is logged with its evidence.

Autonomy level

What the platform may do

Adds root causes and suggested fixes to incidents and PR comments.

AI action logExample

Proposed: rotate payments client certificate (runbook rotate-cert)

Evidence: sig-0007 expiry approaching · owner platform team

  • It proposes, people decide

    It raises PRs and never merges them. Only low-risk actions, such as an approved runbook step, can be applied automatically, and only where a licence enables it.

  • Attacks only in a sandbox

    Attack scenarios run in a sandbox or digital twin, never against live production unless the client approves.

  • Code details never leave the app

    Signals are compact build-time IDs, and the mapping lives only on the platform. Personal data and secrets are redacted before anything is stored or sent to a model.

  • A closed, governed product

    Customers set policies, rules and code style through product screens with fixed options. The models, rules engine and integrations are ours to run and secure.

Deploy anywhere

SaaS or fully air-gapped.

Enterprises first, government-ready from day one.

Hosted by us, with frontier models through our internal gateway.

  • ✓Hosted and operated by FreshField.ai
  • ✓Frontier models through our internal gateway
  • ✓Modular packages
SaaS

Works with what you already run

  • OpenTelemetry
  • Datadog
  • Splunk
  • Elastic
  • Cloud logs

First supported languages

  • Java
  • C#
  • Python

FAQ

Questions, answered.

No. It raises pull requests and people decide. Only low-risk actions, such as an approved runbook step, can be applied automatically, and only where your licence enables it.

How a demo works

Thirty minutes, end to end.

  1. 01

    Pick a time

    Choose a time that suits you, shown in your own time zone.

  2. 02

    See it end to end

    We walk through Runtime Intelligence from raw signals to a fix PR.

  3. 03

    Talk about fit

    We discuss how it fits your services and your deployment.

Stop incidents before they reach production.

See Runtime Intelligence on your own services, as SaaS or fully air-gapped.