Engineering Production-Grade AI

The craft
of intelligence.

ActaClad engineers production-grade AI for the enterprises who refuse to ship what they cannot trust. Through AgentGuard — our agent-native trust platform — and our specialist engineering services.

PLAN user.intent TOOL search_docs() REASON synthesize OUTPUT response TRUST SCORE 98.4 VERIFIED LATENCY 1.24s COST $0.018
85%+
AI Project Failure
of enterprise AI projects never reach production or deliver value. We engineer the ones that do.
100%
Decision Traceability
Every agent decision logged, traced, and audit-ready by design.
60-80%
Cost Reduction
Token economics, smart routing, semantic caching engineered in.
7%
Cost of Non-Compliance
Up to €35M or 7% of global turnover — the maximum EU AI Act penalty.
The state of enterprise AI

Production AI is failing,
and nobody can see why.

Enterprises ship AI that demos beautifully and breaks invisibly. The tools were built for an LLM-call era — not the agent era. Observability, security, and governance live in disconnected silos. The board asks for proof. Engineering shows dashboards. Auditors leave unconvinced.

01

The visibility gap

Agents plan, call tools, hand off to other agents, revise. Today's observability stops at the LLM call. You see the symptom, never the cause.

02

The trust deficit

Boards demand evidence. Regulators demand proof. Engineering teams produce dashboards. None of these speak the same language.

03

The integration tax

Stitching together five tools — observability, evals, red-team, guardrails, governance — costs more than the AI itself. And still doesn't add up.

AgentGuard · The Trust Platform

One platform.
Four capabilities.
Zero compromise.

"AgentGuard unifies what every other vendor sells separately. Because the integration is the value — and that integration cannot be stitched together after the fact."
Pillar 01 / Observability

See every step.
Not just the call.

Agent-native observability built for plans, tool calls, agent-to-agent handoffs, memory state, and replanning loops. Not LLM-call observability with agent features bolted on.

  • End-to-end traces · plans · tool calls
  • Cost · latency · error monitoring
  • Memory state inspection
  • Multi-agent handoff visualization
Pillar 02 / Evaluation

Prove it works.
Across every change.

Comprehensive evaluation framework supporting LLM-level evals (hallucination, accuracy, output quality) and agent-level evals (task completion, plan quality, tool-use correctness). Automated and human-in-the-loop.

  • LLM-as-judge frameworks
  • Agent task-completion evals
  • Human annotation queues
  • Regression detection per release
Pillar 03 / Security

Stress-test like
attackers will.

Static analysis of AI code, dynamic red-team probing across the failure modes that matter, and runtime guardrails — both default policies and custom-authored. Find them in staging, not in production.

  • Adversarial probe library
  • Prompt-injection · jailbreak · leak defense
  • Runtime guardrails · custom policies
  • Continuous re-scoring per release
Pillar 04 / Governance

Audit-ready.
Not dashboard-ready.

Policy frameworks, audit-grade evidence generation, and regulatory mappings to NIST AI RMF, EU AI Act, ISO 42001, and sector-specific frameworks. The Trust Profile your CISO, board, and auditor all understand.

  • Trust Profile · one-page artifact
  • Framework mappings · NIST · EU AI Act · ISO
  • Policy authoring · enforcement
  • Time-stamped audit evidence export
Built For Production

Drop in. Ship agents your CISO will sign off on.

AgentGuard works with your existing stack — LangGraph, CrewAI, raw SDKs, custom orchestration. Production-grade from day one. Audit-ready by design.

ActaClad · Product 01

AgentGuard.

"The trust layer for production agents."

AgentGuard unifies observability, evaluation, red-teaming, guardrails, and governance evidence into one agent-native platform. Drop it into your existing stack. Ship agents your CISO will sign off on.

Agent-native Plans, tools, handoffs, memory — first-class.
Stack-agnostic LangGraph, CrewAI, raw SDKs, custom — all welcome.
Audit-grade Trust Profile artifacts your auditors accept.
Production-first Built for SSO, multi-tenancy, deployment depth.
Trust Score
98.4 / 100
9 dimensions · last updated 2 min ago
Active Traces
14,287
Across 23 agent flows · 3 production environments
EU AI Act Status
Compliant
Article 9 · Article 14 · Annex IV — verified
ActaClad · Product 02

Catch failures before they ship.

Plumbline is an open-source static analyzer for LLM and agentic code. It finds the reliability and architecture defects that make agents fall over in production — deterministically, with no network calls and no telemetry. AgentGuard guards production; Plumbline guards the code before it gets there.

ActaClad · Product 02 · Open Source

Plumbline.

"A plumb line tells a builder whether a structure is true. Plumbline tells you whether your agent code is built to survive production."

A reliability and architecture analyzer — not a style linter. It uses taint and dataflow analysis to reason about real properties of your code: unbounded agent loops, missing fallbacks, unsafe tool calls, silent model swaps. Same code, same findings, every run.

  • Reliability · architecture · harness defects
  • Deterministic — safe to gate a build on
  • No network · no telemetry · runs offline
  • SARIF output · CI-native quality gate
$ plumb scan ./agent
PLB-RES-001 · High · agent/loop.py:42
Agent loop has no iteration cap — runaway risk.
PLB-RES-002 · High · agent/llm.py:18
No fallback when the model provider returns 429.
PLB-SEC-003 · Medium · tools/exec.py:7
Untrusted input reaches a tool-enabled prompt.
3 findings · 0 false positives · 1.2s
ActaClad Services

Engineers who ship,
not consultants who leave decks.

"Most AI projects fail not because the model is bad, but because no one engineered the system around it. We engineer the system."

01 / From Demo to Deployment

Production AI Engineering.

"Build AI that survives real users."

From prototype to production: architecture, reliability engineering, evaluation, and deployment pipelines — so your AI works on day 90 the way it did in the demo.

6–16 week engagements AgentGuard included
02 / AI That Works With What You Have

Enterprise AI Integration.

"AI on top of decades of infrastructure investment."

AI connected to your ERP, CRM, databases, and APIs — no rip and replace. Integration architecture, governance, and phased rollout under real audit and compliance pressure.

12–24 week engagements AgentGuard included
03 / Your AI Engineering Squad, Ready Now

Embedded AI Teams.

"A battle-tested AI team in weeks, not 6 months of hiring."

A senior AI engineering team — Product Owner, Tech Lead, Engineers, QA — embedded in your org. Sprint-based delivery, weekly demos, knowledge transfer built in. Flexible sizing (2–8 engineers).

Retained · 3–12 months AgentGuard included
04 / Stress-Test Before Attackers Do

AI Red-Team & Security.

"Find them in staging, not in production."

Adversarial testing for production AI: prompt injection, jailbreak, PII leakage, tool misuse, multi-agent collusion. Plus runtime guardrail design and continuous re-scoring across releases.

4–12 week engagements AgentGuard included
→ How services and product work together

Services bring us close to real problems. Product is how we scale the answers.

Services are how we learn — being on the ground with customers shows us what production AI actually breaks on. Product is how we deliver that learning at scale. Every services engagement includes AgentGuard implementation, and every product feature is informed by what we saw on a real customer's hardest week.

Why ActaClad

The integration
is the value.

Stitching together a half-dozen point tools is what every enterprise tries first. We built the unified platform because the integration is where trust actually lives.

→ The stitched approach

Five tools, five vendors

Observability, evals, red-team, guardrails, governance — five contracts, five integrations, five upgrade cycles.

Disconnected data

Trace data lives in one tool, eval results in another, red-team findings in a third. Nothing flows together.

LLM-call era tools

Built for the LLM-call era. Agent observability is bolted on. Plans, handoffs, and memory aren't first-class citizens.

Dashboards as output

Every tool produces dashboards for engineers. Auditors and risk committees get nothing they can sign off on.

→ The ActaClad approach

One platform, one contract

Unified observability, evaluation, security, and governance. One vendor, one data model, one integration.

Connected by design

Trace data feeds eval scoring. Eval scoring feeds governance evidence. Red-team findings update the Trust Profile in real time.

Agent-native architecture

Built around plans, tool calls, agent-to-agent handoffs, and memory state from day one. Not LLM-calls with agent paint.

Audit-grade artifacts

The Trust Profile — one page, nine dimensions, signed and time-stamped. CISOs, boards, auditors all understand it in 30 seconds.

Talk to us

Let's engineer your
trusted AI.

Tell us what you're building, your production constraints, and where trust shows up in your stack. We respond within one business day.

For product demos, engagements, red-team work, partnerships, and press.

India
Bengaluru

1st Floor, 27th Main, 13th Cross Rd
1 Sector, HSR Layout
Bengaluru, Karnataka 560102

India
Coimbatore

ThaneerPandhal
Coimbatore, Tamil Nadu

Kottamangalam
Udumalpet, Tamil Nadu 642201

United States
Fort Mill, SC

885 Gold Hill Rd, #3039
Fort Mill, SC 29708

Ship AI you can
actually trust.