Operator-hours
2 s avoidable latency × 60,000 daily transactions ≈ 33 operator-hours per day
Every day. That is four full-time operators standing at terminals, waiting.
Why now
The physical factory has been optimized for forty years. The digital factory has never been measured.
For four decades the semiconductor industry has run the most disciplined continuous-improvement program in manufacturing. Every tool has an OEE. Every process has a Cpk. Every excursion has an owner within minutes. The result is the most productive factory floor humanity has built.
But every one of those tools now waits on software. A lot doesn’t move until the MES commits the transaction. A recipe doesn’t run until a control system validates it. A hold isn’t real until it is enforced at the next step. Dispatch, split, merge, track-in, track-out, ship: each is a database write, a business rule, a call chain through code that someone customized years ago and no one has read since.
That software was designed for a different fab: one with headroom, and a full bench of the engineers who built it. Today the same estate carries a demand cycle driven by AI accelerators, high-bandwidth memory and advanced packaging. It supports processes it was never modeled for. It absorbs acquisitions and site consolidations. It answers to a shrinking group of people who understand it. And every AI initiative the fab is betting on — virtual metrology, predictive maintenance, autonomous dispatch — depends on data flowing cleanly through that same estate.
McKinsey estimated that AI and machine learning could eventually add $85–95 billion a year to semiconductor companies’ earnings, against the $5–8 billion they were contributing at the time: less than a tenth captured. The gap is not ambition and it is not budget. It is the digital factory itself.
The manufacturers that lead the next decade won’t just build smarter fabs. They will build smarter digital fabs.
$85–95B / yr
Long-term annual earnings (EBIT) potential of AI and machine learning for semiconductor companies
Hundreds, often thousands
Interconnected applications in a modern fab’s estate, accumulated over decades of growth and acquisition
~1,200 / day
Application switches per worker in one study; just under four hours a week spent reorienting
The hidden tax
The MES quietly taxes manufacturing. Here is the bill.
Application delays never show up as downtime. They show up as operator-hours, bottleneck capacity, late holds and burned queue time — spread across sixty thousand transactions a day, where no one is looking.
Bottleneck capacity
4 s MES wait × 400 constraint-tool transactions per shift ≈ 5.6% of takt
At the tool you already paid the most for. Capacity you own, spent waiting on a screen.
Late holds
A hold is only real when it is enforced at transaction time. Every minute of lag between “hold applied” and “hold enforced” is nonconforming WIP still moving through the line.
Q-time and outage exposure
A stalled lot burns its queue-time budget. A breach means rework, disposition or scrap. An MES outage doesn’t slow the fab — it stops it, and the recovery backlog runs for hours or days.
What is your MES costing you?
Change any figure; the results update as you type.
Operator-hours lost
33.3
per day
Operator cost
$758,333
per year
Constraint-tool capacity lost
5.6%
of takt at the bottleneck tool
At these numbers, your MES is consuming 33.3 operator-hours a day and 5.6% of your bottleneck tool’s capacity.
How this is calculated
- hours/day = transactions × latency ÷ 3,600
- cost/year = hours/day × $/hour × days
- capacity = (constraint tx × wait) ÷ (shift × 3,600)
These are your numbers, not ours. Bring them to the first call and we’ll tell you where the seconds are going.
Digital paralysis
Six ways the digital factory works against you
Digital paralysis is the inability to execute, adapt or derive value from technology despite heavy investment. In a fab, it has recognizable signatures.
The toggle tax
One lot move: the scheduler for the next lot, the special-work-request app for engineering instructions, SPC to confirm tool qualification, then the MES to execute. Four applications per move, thousands of moves per day, and the cognitive load and delay compound silently.
Late holds and open control loops
FDC flags abnormal equipment behavior but cannot tell the MES to stop the next lot. Metrology results land minutes late. Process windows at advanced nodes are narrower than the lag in the loop that protects them.
The enhancement queue
Process engineers know how to cut a qualification, tighten dispatch logic or remove a step. The change is trapped behind months of release cycle, so the fab’s rate of improvement becomes the software’s release schedule — and engineers build spreadsheets instead.
Data that doesn’t agree with itself
Product definitions, routings, equipment master data and process records live in several systems that were never synchronized. Decisions are made on whichever version loaded first. The discrepancy surfaces weeks later as a planning miss, a quality escape or a yield loss no one can explain.
Technical debt with a production cost
A decade of customization layered into the MES model: hundreds of custom logic functions wired to one event, indexes added without a strategy, timeouts that silently stopped working. Each decision was defensible. The sum is a system that flips from “fine yesterday” to “slow today” after a statistics refresh.
Knowledge that walks out the door
Three people understand how the split/merge logic actually behaves. Two are eligible to retire. The documentation describes the system as it was designed, not as it runs. When they leave, every other risk on this list gets harder to diagnose.
A new discipline
Application Health is to software what OEE is to equipment.
Manufacturing organizations learned long ago that what gets measured gets improved. Equipment has availability, OEE, Cp/Cpk, MTBF and PM compliance, all trended, all with owners. Applications have availability and ticket counts, which say whether the software is on — not whether it is helping or hurting the fab. A system can hold 99.99% availability while adding two seconds to every transaction, fragmenting the data engineers decide on, and blocking the process improvements that would raise yield. From IT’s view it is healthy. From the fab’s view it is a constraint running every hour of every day.
Application Health is the discipline that closes that gap: measuring each application, and the estate as a whole, by whether it enables manufacturing to operate efficiently, adapt rapidly and hit its outcomes. Not once every three to five years in a consulting engagement, but continuously, the way equipment health is measured now.
What each asset is measured by
Equipment
AvailabilityOEECp / CpkMTBFPM compliance
Trended, owned, reviewed every shift.
Applications
AvailabilityTicket countsSpeed under loadData integrityCost to the fab
Says whether the software is on. Not whether it helps the fab.
Six dimensions, and the question each answers
Equipment, automation, AI and people all depend on software. Applications are no longer IT assets. They are manufacturing assets.
Five principles
Treat applications as production assets.
Manage every system that participates in execution by its contribution to fab outcomes, not by service levels alone.
Measure Application Health continuously.
No fab waits three years to inspect a critical tool. Application health indicators belong on the same review as OEE and cycle time.
Build enterprise knowledge as a strategic asset.
Every customization, business rule and interface encodes institutional knowledge. Capture it in the system, not in people.
Modernize with business purpose.
Replace what constrains the fab or blocks the future, not what happens to be old.
Build the foundation for AI.
AI makes decisions only as well as the systems, data and context beneath it. In a fragmented estate it accelerates bad decisions at machine speed.
How Axiarete works
We don’t interview your team about your systems. We read the systems.
The Axiarete platform ingests the artifacts that actually define your digital factory and builds a Technology Intelligence Graph: a living model of what every piece of software does, why it exists, what it depends on, how healthy it is and what it costs the fab.
What we read
- Code repositoriesPortal, code-behind, WCF services, stored procedures
- MES model artifactsDesigner exports: CDOs, CLFs, workflows, business rule handlers, site feature flags
- Configurationweb.config, WCF bindings, IIS, application-pool and site settings
- Logs and telemetryIIS, Oracle AWR/ASH, integration and CIO queue metrics
- Tickets and change recordsIncidents, service packs, enhancements, 24 months of history
- Documentation and financialsPackaged-application docs, run and change costs
Technology Intelligence Graph
Deterministic parsers and decompilers · deep learning · LLMs · agents
What you get
- Purpose and lineageWhy each artifact exists, traced to the tickets and service packs that shaped it
- Business-process mapWhich fab flows (split, hold, dispatch, ship) every customization touches
- Dependency graphCall chains across model, code-behind, WCF, database, labels and integration queues
- Health and churnHotspots ranked by change frequency, size, coupling and fossil code
- Performance findingsPer-row SQL in grid events, unbounded queries, session coupling, empty catches, index-killing predicates
- TCO and rationalizationRun and change cost per artifact; duplicates and dead surface to retire
1Understand
Full visibility into every system, integration, dependency, health indicator, cost and risk — from the code up, not from the org chart down.
2Optimize
Rank every issue by fab impact — frequency × duration × proximity to a bottleneck tool or queue-time window — then remediate: performance, redundancy, technical health, capability.
3Operate
Agentic operations across releases, enhancements, incidents and disaster recovery, with the graph kept current as the estate changes.
The platform
Parses and decompiles every artifact; builds and maintains the graph; scores each application on the six dimensions and the 300-point Technical Health Assessment; ranks findings by production impact; drafts remediations — rewritten queries, index designs, consolidated logic functions, configuration corrections; monitors continuously for regressions and new risk.
Our forward-deployed engineers
Validate every change against recorded production transactions; decide what ships; deploy to one site first; monitor for 72 hours; keep a tested rollback warm; and hand over the knowledge base, test suites and procedures so the capability stays with you.
Neither works without the other. We don’t pretend otherwise.
Solutions for semiconductor
One platform. Six ways to use it against the problems a fab actually has.
Start with the one that hurts most. The same graph powers all six, so every engagement compounds the next.
MES Optimization & Management
Forensic, code-level tuning of Camstar / Opcenter Execution estates: custom code, model artifacts, database, platform configuration and integration. Continuous issue detection, RCA and remediation once the baseline exists.
Outcome40–60% lower shop-floor latency; production configuration risks defused; zero functional regressions across two production tuning engagements.
How MES optimization works →Application Portfolio Rationalization
Reconstruct the knowledge base for hundreds of applications directly from code and configuration; identify consolidation, retirement and modernization candidates with SME-validated evidence; build the business case.
Outcome$11M savings plan on a $40M baseline; 21% of the portfolio identified as reduction opportunity and validated by SMEs; months of manual analysis compressed to weeks.
Application Modernization & MES Migration
Feature-level replaceability assessment against Siemens Camstar / Opcenter and SAP; auto-generated migration plans, test cases and requirements; risk discovery for version upgrades and post-M&A site consolidation.
Outcome90% accurate replaceability assessment validated by in-house experts.
Technical Debt & Risk Reduction
The 300-point Technical Health Assessment across architecture, quality, performance, security and governance; code-level drill-down; business-impact modeling per issue; agentic remediation for the issues that matter.
Outcome50–80% reduction in architect and developer effort to understand and remediate issues; a standing foundation for enterprise technology risk governance.
Business Process Intelligence & Optimization
End-to-end mapping of fab processes across the systems that execute them, with health, performance and cycle time per step; identification of friction, manual reconciliation and the toggle tax; optimization to reduce cycle time.
OutcomeProcess bottlenecks removed at the point where software, not equipment, is the constraint.
Application Security
Unified risk discovery across source code, software supply chain and runtime for the applications that touch the fab; qualification by exploitability and production impact; agentic remediation.
Flagship: MES optimization
We know where Camstar hides its seconds.
Generic .NET and Oracle advice doesn’t fix an MES. Axiarete’s analysis targets MES constructs directly — grid display events, custom logic functions, business rule handlers, data objects, site feature flags, integration queues — with the failure modes known for each.
01Measure and attribute
- Tag every database connection with service, site and user, so slow work traces to a named transaction, not to “the app” or “the database.”
- Log duration, SQL statement count, rows returned and errors for every transaction.
- Rank transactions by fab impact: frequency × duration × proximity to a bottleneck tool or a queue-time window.
02Custom code health
- 300-point Technical Health Assessment of all custom code — C#, ASP.NET, XML, embedded databases — across performance, stability, scalability, security, maintainability and compliance.
- Discover, prioritize by business impact, remediate.
- Rewrite the queries that quietly govern response time.
03Database tuning
- Pin execution plans for the highest-volume transactions so performance doesn’t flip overnight after a statistics refresh.
- Add the indexes that a decade of custom queries never got.
- Move statistics jobs, index rebuilds, purges and model activations out of production shifts.
04Platform configuration and stability
- Correct the timeout sequence so each layer times out before the one beneath it: browser, then WCF, then database.
- Find the debug flag that silently disabled every timeout in production, and turn it off safely.
- Set WCF message and object limits so an oversized response returns an error instead of exhausting the application pool.
- Move session state out of process so pools can be recycled mid-shift and servers can be added.
05Integration and hold enforcement
- Monitor integration queue depth and the age of the oldest message; a growing backlog means lots are waiting on data, not equipment.
- Treat label and print failures as shipment blockers with a defined recovery.
- Measure the delay between a hold being applied and being enforced.
- Check hourly for lots in inconsistent states and quarantine them before further transactions compound the damage.
Anatomy of one transaction
MoveOut · lot at a constraint tool
- Operator click to requestbrowser → IIS
- Session lock and WCF callin-process session state
- Custom logic functions before commitCLFs wired to one event
- Grid display event, one query per rowper-row SQL
- History read without an indexfull table scan
- Plan that flipped after a statistics refreshunpinned execution plan
- Label print and integration queuebacklog ahead of the message
- Hold enforced at the next stepapplied ≠ enforced
Time the operator waits
What you get: the Camstar optimization command center
A standing capability, not a report. It keeps running after the engagement ends.
- An inventory of every customization in the system
- Full technical health assessment
- Continuous issue detection (system errors, data-connection failures), RCA and remediation guidance
- Quick-win improvements
- Alerts and written incident procedures for maintenance
- A prioritized roadmap of performance optimizations
- An evidence base that carries directly into the Camstar V7 → Opcenter Execution upgrade when you’re ready
Proof
Two production Camstar estates. Every number below is from the engagement.
Both engagements were forensic: we decompiled the estate, measured every transaction, and rewrote what the evidence pointed to. Every rewrite was validated against recorded production traffic before it shipped.
Engagement 1
Model-level tuning of a production Camstar estate
Camstar CEP 7.3Opcenter Execution SemiconductorOracle
0functional regressions; every rewrite golden-master validated
The situation. Shop-floor grids and track-in / track-out had slowed unpredictably. Execution plans flipped overnight: fine yesterday, slow today.
Forensic scope
1,299SQL queries decompiled
1,760index definitions mapped
1,638 / 23,043tables and columns profiled
843custom functions traced
| Finding | Before | After | Count |
|---|---|---|---|
| Setup-matrix lookups sorted entire tables to return one row | ROW_NUMBER() over a 25-level CASE sort after 28 joins; every row materialized | Indexed specificity score + FETCH FIRST 1 ROW ONLY; stops at the first row | 47 matrix queries re-engineered |
| Grid columns executed one query per displayed row | 16-join scrap-quantity query per row; a 100-row lot grid drove 400 history scans | Lookup folded into the grid query as a set-based join; one scan per render | 400 → 1 history scans per grid render |
| Predicate shapes that disabled every index | (col LIKE ? OR col IS NULL) stacked 20 deep; LIKE on Boolean and Integer columns | Exact-match / wildcard UNION ALL branches with typed equality; range scans restored | 365 index-killing predicates rewritten |
| Index estate and execution plans rebuilt on evidence | 125 redundant, 16 empty and 20-column-wide indexes; 20 on the hottest table alone | B-trees on 205 verified join paths; monitored drops; SQL Plan Baselines pinned | 125 redundant indexes retired |
400→1
history scans per lot-grid render
365
index-killing predicates rewritten; range scans restored
125
redundant indexes retired; B-trees on 205 verified join paths
47
setup-matrix queries now stop at the first row
Also
102 copy-pasted revision self-joins eliminated at source
417 unbounded grid queries given row caps
5 production configuration risks defused, including a debug flag that had disabled all timeouts
0 functional regressions — every rewrite golden-master validated
Engagement 2
Metadata-level tuning of a production Camstar estate
Camstar OpcenterSemiSuiteOracle 19c
40–60%lower shop-floor latency
The situation. Lot MoveOut, TrackIn / TrackOut and 2D Lot Start had slowed as a decade of customization layered into the model database. Thousands of metadata calls queued ahead of every commit.
Forensic scope
85 MBmodel database decompiled end to end
2,064runtime tables profiled
682nested-subquery patterns mapped
5,000+function calls traced per MoveOut
| Finding | Before | After | Result |
|---|---|---|---|
| One MoveOut fired thousands of metadata functions before commit | 110 custom logic functions wired to one event: 2,696 calls; seven SkipPlan CLFs alone burned 424 | Early-exit consolidation; printing and trace codes moved to AfterCommit | 2,696 → 1,500 functions per MoveOut; latency down 40–50% |
| A sixth of the schema had no index; history queries scanned tables | 335 of 2,064 tables bare; 22 phantom indexes defined with zero columns | Composite B-trees on history mainline (ContainerName, TxnDateGMT, CDODefId) | 335 → 0 unindexed tables; history reads up to 80% faster |
| Every dispatch recomputed a static CDO hierarchy, recursively | 254 queries ran CONNECT BY / derived-CDO lookups over an unchanging tree | Hierarchy materialized, refreshed on deploy | 254 recursive lookups materialized; 1–4 s back per sequence |
| Dispatch grids fetched all 190 container columns to display 15 | 48 SELECT * queries; every default dispatch grid pulled the full row | CDO queries column-pruned to the 15 shown | 190 → 15 columns; payloads down ~90%; 1–5 s per dispatch list |
2,696→1,500
functions fired per MoveOut
335→0
unindexed tables
190→15
columns fetched per dispatch grid
254
recursive CDO lookups materialized; 1–4 s back per sequence
40–60%lower shop-floor latency across MoveOut, TrackIn / Out and 2D Lot Start
20–30%of the gain landed in the first 2–4 weeks as quick wins
51PL/SQL
TABLE() context-switch calls re-engineered60–70%less I/O per lot after splitting the 309-column attributes table
Application rationalization across a manufacturing portfolio
Reconstructed the knowledge base for a manufacturing portfolio from 12 million lines of code; ran the Technical Health Assessment on every application; distilled three rationalization scenarios.
- 21%of the portfolio identified as reduction opportunity and validated by SMEs
- $11Msavings plan contributed against a $40M baseline
Modernization and Camstar replaceability assessment
Archaeological knowledge construction for hundreds of legacy manufacturing applications; feature-level replaceability assessment against Siemens Camstar and SAP; auto-generated migration plan, test cases and requirements.
- 90%accurate replaceability assessment, validated by the in-house expert
- Completetest cases and risk mitigation built for migration planning
How an engagement runs
Eight weeks. First findings in week two. Quick wins in production by week four.
No workshops before evidence. Analysis starts from your code on day one, and the executive review at the end of week eight decides whether to continue, change scope or stop — on results.
- Weeks 1–2
Analyze the system and performance trends
Analyze code, configuration, logs and incident records; produce an inventory of every customization. Add measurement: response time and database usage per transaction. Map the 8–10 transactions the fab depends on — move, split, hold, print, ship — and agree how incidents will be ranked by production impact.
- Weeks 3–4
Publish the baseline and first quick wins
Performance and KPI baseline by key transaction. Full inventory of customizations and every site-specific setting documented. First low-risk fixes discovered, prioritized and delivered — for example, correcting the setting mismatch that logs operators out mid-transaction.
- Weeks 5–6
Build the optimization roadmap; keep optimizing
Full roadmap prioritized by business impact and execution difficulty. Continue delivering — for example, timeout corrections so a slow step fails fast and releases its locks instead of blocking other users. SME reviews to validate and refine.
- Weeks 7–8
Finalize the roadmap, register the wins
Validate every optimization and tuning opportunity. A twelve-month plan: optimizations, priority, action plans. Executive review: continue, change scope, or stop.
Deliverables at the end of week 8
- An inventory of every customization in the system
- Measured response times for the key transactions
- A ranked list of specific defects, each with a defined fix
- Quick-win improvements already delivered
- Alerts and written incident procedures for maintenance
- A prioritized roadmap of performance optimizations
How we protect production: the Axiarete change protocol
- No production change before baselines exist
- Every release tested against recorded production transactions
- Deployed to one site first
- Monitored for 72 hours
- Rollback tested before use — never assumed
Data to get started
- Custom application code and the deployed release package
- MES model export from Designer, including site feature-flag values
- Application, WCF and IIS configuration files
- Database structure, procedures and workload reports
- 90 days of system, application and integration logs
- 24 months of incident and change records
We can start with your top 2–3 of these.
Access and time
- An MES user account and a read-only database account on your stage environment
- Remote access by your standard method
- A project lead for 2 hours a week
- SMEs for 4–8 hours once for data access and 4–6 hours once for validation
- A sponsor for 1–2 hours total, at kickoff and the week-8 review
Why fabs choose Axiarete
What typical MES consulting does, and what we do instead
Typical MES consulting
The Axiarete standard
Surveys, interviews and workshops; little attention to code or configuration
Rigorous, bottom-up AI-driven analysis of the current state — files, model artifacts, configurations — so issues are prioritized and remediated across the whole estate, not piecemeal
Slow lead time, gated on workshop and interview availability
First findings in week two. Analysis starts from code on day one; the full baseline — response time per transaction, classified incident history, custom code inventory — is published by week two
Generic .NET and Oracle performance advice
Analysis targets MES constructs directly: grid display events, custom logic functions, business rule handlers, data objects, site feature flags, integration queues — with the failure modes known for each
Engagement ends with a recommendations document
Option to have Axiarete forward-deployed engineers execute — application code, configuration, database objects and Camstar model — and verify each change
Change windows with no tested way back
No production change before baselines; each release tested against recorded production transactions, deployed to one site, monitored 72 hours, with a rollback tested before use
Knowledge leaves with the consultants; the upgrade is a separate project later
The knowledge base, test suites, scripts and procedures are handed over and kept current. The same evidence base supports the Camstar V7 → Opcenter Execution upgrade when needed
And versus portfolio tools. CMDB, APM, TBM and EA tools are recordkeepers: they hold what you tell them, decay faster than teams can update them, and answer “what exists,” not “what to fix, retire or modernize, and why.” Axiarete reads the systems themselves and keeps the answer current.
Security and data handling
Your code is your process. We treat it that way.
Fab application estates encode process IP, routings, recipes and supplier relationships. We handle them under a signed NDA and a written data-handling agreement before any artifact moves, and we work from your stage environment with read-only database access wherever the work allows.
SOC 2 Type II
ISO/IEC 27001:2022
ISO/IEC 42001:2023
Read how Axiarete governs AI and data →
- SOC 2 Type II attestedSecurity controls independently audited over time.
- ISO/IEC 27001:2022 certifiedInformation security management.
- ISO/IEC 42001:2023 certifiedAI management system, covering how models are governed, evaluated and controlled.
- A dedicated environment for each customerA dedicated AWS environment with AES-256 encryption, customer-managed keys and zero-trust access; data residency defined by agreement.
- Your code never trains a modelCustomer code, models and logs are not used to train Axiarete or third-party models.
- Your data leaves with youExport available for 30 days after termination; secure deletion within 90 days, backups included.
Perspectives
How we think about the digital factory
Axiarete PerspectivesThe Digital Factory Operating System
August 2026
The Digital Factory Operating System
Why application intelligence has become the next competitive advantage in semiconductors. Introduces Application Health and its six dimensions, and the model for connecting software health to throughput, yield, cycle time and OEE.
Read the paper →Axiarete PerspectivesEscaping Digital Paralysis in the Semiconductor Industry
July 2026
Escaping Digital Paralysis in the Semiconductor Industry
How complexity is slowing decisions, eroding value and stalling AI. Four field cases from the authors’ careers in fab IT, what they have in common, and a 90-day path out.
Read the paper →Axiarete PerspectivesSystems Are the Last Frontier of the American Manufacturing Revival
August 2026
Systems Are the Last Frontier of the American Manufacturing Revival
Robots will close the labor gap and lean will keep grinding out process gains. The software that runs our factories is what stands between reshoring announcements and reshoring results.
Read the essay →Start here
Start with your top two or three inputs. We’ll show you where the seconds are going.
Send us the custom code and the Designer model export — or the database workload reports and 90 days of logs — and within two weeks you will have a measured baseline of your key transactions, an inventory of every customization, and the first ranked list of what to fix. No workshops first. No slide deck about your industry. Evidence from your own estate.
Two hours a week from a project lead. Stage access only. An executive decision at week eight, on results.
✓
Thank you. We’ll be in touch.
We’ve received your request and will reply shortly to agree which inputs to start from.
FAQ
Frequently asked questions
Which MES platforms do you support?
Our deepest experience, and both production tuning engagements, are on Siemens Camstar / Opcenter Execution Semiconductor running on Oracle, with .NET / WCF customization. The Application Health discipline and the portfolio, modernization and technical-debt solutions apply across the estate regardless of MES vendor.
Do you recommend optimizing what we have, or replacing it?
Both, in the right order. The platform first shows you which applications constrain the fab and which are redundant. You retire what is redundant, modernize what blocks the future, and optimize the core you are keeping — including the MES you will be running for years either way. The same evidence base carries into a Camstar V7 → Opcenter Execution upgrade when that is the right move.
Is this software, or a consulting engagement?
It is a platform operated with you by forward-deployed engineers. The platform reads, models, scores, ranks, drafts and monitors; the engineers validate, ship, watch and hand over. The platform section above states exactly which is which.
Where is our code processed, and does it train your models?
Each customer operates in a dedicated AWS environment with AES-256 encryption, customer-managed keys and zero-trust access, with data residency defined by agreement. Customer code, models and logs are not used to train Axiarete or third-party models. See Security and data handling above.
How quickly will we see results?
First findings in week two. Quick wins — typically configuration and timeout corrections, index additions and the highest-volume query rewrites — are in production by week four. In our second tuning engagement, 20–30% of the total latency gain landed in the first two to four weeks.
What do you need from our team?
A project lead for two hours a week, subject-matter experts for roughly a day in total across the eight weeks, and a sponsor for two hours. Everything else we take from the artifacts.