“LOCI compares the compiled binary against master — and comments on every Pull Request before merge.”
89%
of teams had a production incident from AI-generated codeSource
LOCI Catcheswhat coding agentsmiss.
“You’re right. I missed that.” · AI coding agent
Agents read source, not how it runs.LOCI models your BIN on real HW.Catches the confident miss, at /plan.A coding agent reasons about your source,not how it runs on your HW: time, energy, memory, CPU.LOCI models how your BIN runs on your HW, catching what’s confident but wrong, at /plan.
AI Physics · Trained on Real-HW traces · No runtime · No instrumentation
The catch · before a line shipped
loci · plan review
User
Encrypt session tokens with OpenSSL AES-256-GCM.
Agent · Plan
Call EVP_CIPHER_CTX_new() per request, then encrypt + free.
LOCI
On target CPU, EVP_CIPHER_CTX_new() alloc/free dominates the hot path — worst path +1.8 ms/req. Push back: reuse the context.
evidence ·worst +1.8 ms/req · p99 +12 ms
Agent · Adjust
Good catch — cached EVP_CIPHER_CTX, reset per call. +0.05 ms/req. ✓
Plan-time catch · OpenSSL per-request alloc on the hot path.
Trusted by partners, customers & investors
Sound familiar?
Coding agents are fast, tireless, and usually right. But they have three tells.
Babysitting. Walk away from the agent and it drifts.
Only 3% of developers highly trust AI output. Stack Overflow 2025
Bluffing. Confident, well-formatted — and wrong.
“Clean syntax, no runtime errors” — and entirely fabricated. Replit CEO
First-pass misses. Iterating toward what should've been right.
66%’s top frustration: “almost right, but not quite.” Stack Overflow 2025
All three trace to one thing: it can’t see how its code runs.
Replit AI bluff
“All done — and no, the rollback won't recover it.”
Wiped the production DB, faked 4,000 users, lied about recovery.
The SolutionNot a GPT wrapper
Catches what your agent gets confidently wrong.
AI Physics — trained on real-hardware traces from real CPUs and GPUs.
ble-gateway — agent
USERcut power on the BLE gateway.
PLANoptimize peer_scheduler_on_notify compute.
LOCIconn-event energy dominates ~100× — 59.6 µWs/event vs 0.07. The wake cadence is the cost.
ADJUST PLANGood catch. optimize conn-event frequency, not per-notify compute.
loci · cockpit live
LOCI ble-gateway · main · 5 catches · 27 checks · 69 fns
Catches
13:43post-edit⚠ CAUTION
Value · this branch
First-pass76% clean · 0 plans stopped at /plan
Pushbacks1 hard · 5 caught pre-merge
Babysitting≈10h saved (est.)
LOCI→agent16 raised · fixed · shipped
The cockpit — live beside your agent
Read more ▾
The agent plans; LOCI catches the confident-but-wrong; the cockpit mirrors it as a quantified signal — first-pass, pushbacks, hours not babysitting — live.
PushbackConn-event energy dominates ~100× · plan redirected before code.
→Coding agent“Good catch — optimize conn-event frequency, not per-notify compute.”
The SolutionNot a GPT wrapper
Catches what your agent gets confidently wrong.
AI Physics — trained on real-hardware traces from real CPUs and GPUs.
- Predicts CPU timing, energy, stack & memory
- Quantified signals: first-pass, babysitting, pushbacks & warnings
- Contract envelope keeper
- No instrumentation · no runtime · agent-agnostic
Real catches
Seven times the agent was sure. Seven times LOCI had the proof.
Every one from a real session — firmware, silicon, LLM inference. Numbers you can check. →
LOCI · BLE firmwarecaught
The hot loop wasn't the cost — the radio was.
Agent was optimizing the wrong thing. Redirected at /plan.
100× off
The outcome
Babysitting
watch every step
it flags the quality regression before merge
First-pass
iterate until it's right
the plan is grounded before code
Bluffing
confident, untested claims
every claim is backed by a real measurement
Same proof, every altitude — execution truth for the engineer, quality regressions caught for the lead, hours not babysitting for the team.
Independent Validation
LOCI in the loop, not in the way.
An independent validation layer at every stage of the agent loop — plan, write, PR, and merge.
Coding agent · Claude Code · Cursor · Copilot
PlanWritePRMerge
LOCI
The Engine·AI Physics
LCLM small models trained on what general LLMs never see.
Behavioral models trained on real hardware traces that predict how a binary behaves on the target silicon — real CPUs and GPUs. That is AI Physics — a small specialist that catches the behavioral regressions source-code-only LLMs miss, at a fraction of the cost.
NVIDIA / AMD / ARM
real-hw runs
Trace
execution data
LCLM
execution model
LOCI Guardian Agent
agent in the loop
Deterministic
Bounded by physics
Verifiable on hardware
Human-on-the-loop
~220×cheaper per query vs frontier LLMs100s of %source-LLM prediction drift120+patents6 yrsreal-hw training
Not a GPT wrapper
Autonomy · Opt-in
Earn LOCI's autonomy. On your terms.
Hand off at your pace. Each rung is opt-in.
○Watch
Silent surface. Evidence on demand. Zero PR noise.
◐Advise
PR comment with verdict + drill-down. Never blocks.
◑Gate
Blocks merge on critical-gate failure. Advises on the rest.
●Auto
Auto-action — block · revert · escalate. Notifies you after.
NewEDA & pre-silicon
We're bringing execution signals to RTL teams.
RTL execution intelligenceGround your Verilog in the customer’s real workload.
No sim re-run per RTL change. LOCI attributes every cycle, prices decisions before silicon.
✓ Customer workload, not synthetic benchmarks·✓ No sim re-run per change·✓ Fits your verification stack
Workload binary
ELF · firmware · kernel · inference
LOCI analysis
Execution signals
EDA / RTL stack
Sim · emu · copilot · verify
Pre-silicon · workload → execution signals → EDA stack
“Throughput-per-Watt and CPU/GPU hotspots predicted in the CI/CD pipeline — without source-code access.”
AWS Marketplace
LOCI Performance Intelligence · AWS Marketplace
“Binary line-of-code updates over NTT DATA's Private 5G expand cellular broadcasting capacity from 1,000 to 5,000 machines per radio cell.”
NTT DATA
LOCI LCLM × NTT DATA Private 5G
“LOCI's binary delta engine creates compressed functional updates up to 97% smaller — cutting cloud transport costs across major fleets.”
Deutsche Telekom · T-Systems
LOCI delta engine × T-Systems cloud
“On AURIX™ TC4x, LOCI 2.0 enforces Quality Contracts — blocks releases that exceed stack-depth or latency budgets.”
Infineon Technologies
LOCI 2.0 · AURIX™ TC4x
“Runs on the NPU as a Reliability, Availability and Serviceability engine — flags risky functions before they fail.”
STMicroelectronics
LOCI RAS Engine · ST Partner Ecosystem
Safety-critical DNA
Built to the standards your compliance team already trusts.
8 years shipping into automotive and industrial systems. LOCI inherits the rigor.
ASPICE Level 2
Automotive software process maturity
ISO 26262 / ASIL-B
Functional safety for automotive
ISO 21434
Cybersecurity engineering for road vehicles
Autonomous Vehicles
Production AV programs · ISO 21448 / SOTIF aligned
ISO 27001
Information security management
120+ Patents
Binary analysis & execution modeling
Works with the tools your team already uses
- Platformself-hosted · SaaS
- GitGitHub · GitLab · Bitbucket
- AzureDevOps · pipelines
- AWSMarketplace listing
- Claude CodeMCP plugin
- CopilotCLI hook · skills
- GCC+ Clang · LLVM · MSVC
Specialist vs Frontier
Could you ask a frontier LLM instead?
You could. It would cost ~220× more per query — and miss by hundreds of percent. Behavioral prediction needs real execution traces, not source code alone.
Frontier LLM·source code only
- Trained on
- Source code
- Predicts from
- Patterns
- Accuracy
- 100s of % drift
- Cost per query
- ~220× more
LOCI·trained on execution
- Trained on
- Real hardware traces
- Predicts from
- Execution behavior
- Accuracy
- Trace-validated
- Cost per query
- Small specialist · efficient
Small specialist + real execution > frontier LLM + source code.
Guard every coding agent decision with execution evidence.
Your agents are already shipping decisions. LOCI gives every one — plan, PR, merge — runtime-grade evidence the agent and reviewer can act on.
75%
of AI coding agents introduce quality regressions during long-term maintenance.Source