Generative AI Consulting Services
Mactores is the agent-native AWS modernization firm. Additionally, generative AI consulting services are delivered around one commitment: agents that reach production, on a fixed date, at a fixed fee.
Most generative AI programs don't fail at the model. They fail at the handoff, the point where a working prototype has to become a system a business runs on, with an owner, an audit trail, and a production budget line. Mactores' forward-deployed FDEs own that handoff, from architecture through cutover, on a date fixed in the contract. Mactores absorbs the cost of any delay it causes.
Talk to us
Agents that reach production, on a fixed date, at a fixed fee.
You leave knowing whether the date is reachable and what the work actually involves.
- AWS Services Partner
- Premier Tier
- AWS Specialization
- Agentic AI
- Incl. Migration & Modernization
- 7 Competencies
- AWS Service Validations
- 17 Validations
60–70%
of the hours an equivalent conventional proposal would staff as analyst time, carried by agents
12 wks
KlearTrust's full cycle, scoping through hypercare
21
public case studies with named customers
200+
AWS-certified FDEs
18 yrs
building on AWS
Top Mactores Clients
Where Do Generative AI Programs Actually Die?
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01
Retrieval that decays quietly
Source documents change, embeddings go stale, the corpus outgrows the index it was tuned for. None of that shows up as an error. It shows up as answers that get gradually worse, weeks after everyone stopped watching closely.You're in this one if
Your evaluation plan was a demo script, and nobody has re-run it against this month's data.
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02
Tool calls and decision rights that were never written down
A scripted demo exercises the happy path. Production traffic doesn't. The failure isn't just “the agent called the wrong tool”. It's that nobody defined, in writing, what the agent is allowed to decide versus required to escalate, so the first edge case becomes an incident instead of a handled exception.You're in this one if
You can't point to a document that lists what your agent must never do without a human sign-off.
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03
Unit economics nobody modeled past the pilot
Token cost and latency at ten test users look nothing like token cost and latency at production volume. Programs that budgeted for a pilot discover their real per-interaction cost, or their real p95 latency, only after go-live, which is the worst possible time to find out the math doesn't work.You're in this one if
Your cost-per-interaction number was calculated once, before launch, and never since.
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04
Escalation paths invented mid-incident instead of designed in advance
Most pilots have no answer to “who gets paged when the agent is confident and wrong?” until that scenario actually happens. Escalation paths, review sampling, and override authority get invented under pressure instead of being part of the architecture.You're in this one if
Your rollback plan is “turn it off,” with no defined path back to a human reviewer.
Scope around the real risk
A fixed date forces every failure mode onto the table before build starts.
All four are cheaper to find during scoping than in production, and none of them surfaces on its own.
Who Actually Delivers It?
Every FDE on this engagement is an agentic AI specialist whose day job is shipping agents on AWS in production, rather than a generalist consultant learning the pattern on your project. They embed with your team for the length of the engagement, make the judgment calls tooling can't, and carry the delivery date personally. That is the difference the comparison table further down turns on: the alternatives staff a bench, this staffs an owner.
Behind them: 200+ AWS-certified FDEs, 21 public case studies with named customers, and eighteen years building on AWS. Those are supporting credentials rather than the pitch. The pitch is the system that reaches production on the date in the contract.
The commitment, in writing
The clause behind the date, and who carries the cost when it moves, is set out in full on the commitment page.
Read the clause that carries the date, the fee and the overage before any conversation about scope.
Is This the Right Engagement for You?
This is built for you if
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You have a working prototype or pilot that's stuck getting sign-off to go to production.
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You have an external date attached, whether a board commitment, a regulatory deadline or a release train, that can't move.
- Your data and integration surface is messy enough that no internal team wants to own the estimate.
- You need audit-grade evidence a regulator or internal audit function will accept, rather than a demo that impressed a steering committee.
This isn't the right fit if
- You're still deciding whether an agent is the right pattern at all. That is a discovery conversation, and a different scope from a fixed-date build.
- You want an open-ended, time-and-materials engagement with no ship date and no delivery accountability attached to it.
- You need a multi-year, multi-agent platform build-out run by an embedded program-management office. That is a different scope and a different shape of engagement.
- Your data isn't accessible enough yet to support a scoping conversation within weeks. That is a data-readiness project first.
AI Agents for Apps
Need a focused path to production? The AI Agents for Apps pillar page sets out the wider practice this engagement sits inside.
The commitment page carries the delay clause in full.
The Engagement, Phase by Phase
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01
Scoping under a fixed date
Typically 2–4 weeks
Agents conduct discovery on your data and integration surface through retrieval-readiness mapping, tool-call inventory, and evaluation-scope scoring. Forward-deployed engineers architect the target agent design. The statement of work documents scope, ship date, and fixed fee, signed before build begins.Phase exit
Scope, ship date, and fixed fee, signed before build begins.
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02
Build against the evaluation harness
Typically 6–10 weeks
Agent build and integration happen against an evaluation harness that runs parallel to your live data, checking retrieval precision and recall, faithfulness to source documents, tool-call success rate, latency, and cost per interaction at projected volume. Customer-signed acceptance checkpoints occur before each phase closes.Phase exit
Customer-signed acceptance checkpoints before each phase closes.
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03
Cutover
Typically 1–2 weeks
The engagement plans and rehearses cutover execution. Rollback thresholds are written down and agreed with your team before the window opens, so nobody is inventing the trigger at two in the morning.Phase exit
Rollback thresholds written down and agreed with your team before the window opens.
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04
Tuning in production, then handoff
Typically 2–4 weeks
FDEs tune performance against real production traffic and usage volume. Hypercare closes with a documented handoff of runbooks, architecture documentation and knowledge transfer, converting the cost structure from pilot spend to production ownership.Phase exit
Documented handoff of runbooks, architecture documentation and knowledge transfer.
These ranges are illustrative, sized against engagements of moderate complexity. The four phases total eleven to twenty weeks at that complexity.
Your own ranges get fixed only in the signed SOW after scoping. For scale: KlearTrust's full cycle, scoping through hypercare, ran twelve weeks against a prior six-month manual process.
Start at Phase 1
This is a production engagement: scoped, dated, and priced before the first agent ships.
Scope, ship date and fixed fee, all signed into the statement of work before build begins.
Proof, Not Promises: What's Already Shipped
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Agentic AI · Healthcare
12 wks
Production system, replacing a six-month manual cycle
0
Remediation cycles on HIPAA and internal audit review
KlearTrust: HIPAA-aligned agentic claims platform
Six-month manual claims-review cycle replaced with a twelve-week production system. HIPAA and internal audit review passed on first submission, with zero remediation cycles and evidence available on request.
Read the case study -
Agentic AI · Telco
80%
Routine intents the agents now clear
Intent-based
Replaced a scripted decision-tree bot
Safaricom: production agentic customer experience
For Africa's largest telco, a scripted decision-tree bot was replaced in production with intent-based agents that resolve on stated intent rather than a fixed menu tree. Human agent capacity was reallocated from the 80% of routine intents the agents now clear, to the cases that genuinely need a person. Escalation is intentional rather than a symptom of the system failing.
Read the case study -
Agentic AI · Legal
50–80%
Efficiency gain on Office Action responses
Up to 40%
Reduction in procedural errors
Sterne Kessler: agentic patent prosecution, attorneys in the loop
One of the most demanding patent prosecution practices in the United States was spending eight to twelve hours of attorney time on every Office Action response. The production system built on Amazon Bedrock brought that to two to four hours, a 50 to 80 percent efficiency gain on the highest-cost line in the practice, with up to a 40 percent reduction in procedural errors. Human-in-the-loop controls preserve attorney oversight at every consequential decision, and the audit trail is captured at decision time rather than reconstructed afterwards.
Read the case study
All three are drawn from Mactores' set of 21 public case studies with named-customer references across data platforms, applications, and agents.
Ask for the closest reference
We will name the engagement nearest your stack, not the category.
All three are drawn from Mactores' 21 public case studies with named-customer references.
Where This Differs From the Alternatives You're Probably Also Considering
"Generic generative AI consulting" isn't one thing. The buyers who reach this page are usually comparing against one of four alternatives, and each trades off differently:
| Alternative | What it's good at | Where it tends to cost you |
|---|---|---|
| Big 4 / global advisory | Steering-committee buy-in, governance frameworks, board-level narrative. | Delivery teams often staffed with generalists learning agentic patterns on your engagement; billing usually stays time-and-materials with no delay cost to the firm. |
| Tier-1 systems integrator | Delivery scale, bench depth, ability to run a multi-year program. | Priced and staffed for programs rather than one workflow on a named date. Scope tends to grow to fit the team more often than the team shrinks to fit the scope. |
| AWS ProServe | Deep platform fluency, proximity to the AWS roadmap. | Engages inside AWS's own commercial motion, typically sized for platform migration over a single agent's path to a production cutover date. |
| In-house build | Full context on your data, your users, your politics. | Production ML-ops and evaluation-harness discipline for agents specifically usually isn't the team's day job. It gets built once, under pressure, during the first incident. |
Mactores' position: agent-native by structure rather than a generalist practice with an AI slide added to the deck, scoped to one production workflow, with a fixed date the firm carries financial exposure against. How that delivery model works across every engagement is set out on the how we work page.
Agent-native by structure
Agent-native by structure, not a generalist practice with an AI slide added to the deck.
How that delivery model works across every engagement is set out on the how we work page.
Audit-Readiness, Framework by Framework
Validation harnesses and phase-exit sign-offs are built to produce audit-ready evidence, a standard well above a passing internal test, for frameworks including HIPAA, PCI-DSS, SOC 2, CCPA, FFIEC guidance, SEC alignment, and NIST's AI Risk Management Framework, depending on your industry and data:
Audit-ready by default
Evidence an examiner will accept, produced as a byproduct of delivery.
Bring the scope your examiner cares about and we will map it to the phase exits that produce the evidence.
How Does the Engagement Change by Industry?
The commitment — fixed date, fixed fee, Mactores-absorbed overage risk — doesn't change by industry. What changes is which risks get emphasized in scoping.
Financial Services
Regulator-grade validation, FFIEC/SEC alignment, agent-decision lineage as an audit artifact.
Financial servicesHealthcare & Life Sciences
HIPAA-aligned architecture, clinical workflow integration using Amazon HealthLake and Amazon Comprehend Medical.
HealthcareInternet & Software
App-to-agent conversion synchronized with your existing release cadence.
AI Agents for AppsManufacturing
Continuous operations during cutover; validation runs parallel to live operational data from Phase 1.
ManufacturingTelco, Media, Entertainment, Gaming, and Sports (TMEGS)
Real-time performance validation; peak-load observability built into the evaluation harness pre-cutover.
TMEGSThe delivery model stays the same in every vertical. What changes is emphasis: governance for regulated data, continuity for operational systems, speed for product-led teams.
Scoped to your sector
The commitment holds across industries. Which risk gets scoped first is yours to name.
Regulator-grade validation, HIPAA-aligned architecture, release-cadence sync or peak-load observability.
What Drives the Fixed Fee
Pricing disclaimer
Nothing in this section is a quote. Every figure in this section is a directional planning context. Your fee is named after scoping, against your systems, and confirmed in the statement of work you sign before build begins.
Where the funding comes from
Already inside your existing spend
- The manual review workflow an agent replaces
- The support or ops headcount a conversational interface offsets
- An integration layer that already bills hours just to stay running
One illustrative scenario
As one illustrative scenario rather than a claim about your numbers, take a claims or back-office review queue processing 1,500 items a month. At 25 minutes of reviewer time each, that is roughly 625 hours a month, and at a fully loaded reviewer cost of $45 an hour it comes to close to $340,000 a year sitting inside a workflow an agent can fully or partially absorb, funded from budget that already exists rather than a new line item.
Both inputs are placeholders for your own. Substitute your queue volume, your handling time and your loaded hourly cost; the arithmetic is shown so you can.
Make the numbers yours
Put your own queue volume, handling time and loaded hourly cost against the drivers above.
Half an hour with the engineer who would scope the work, and the fixed fee stops being a range.
What Do These Terms Mean in a Proposal?
These terms carry specific meaning in how Mactores scopes and delivers this work. Use this section as a reference while reading the rest of the page.
- Agent-native
- A structural property of the firm rather than a technique it uses. Three things are true at once. Agents absorb most of the engagement hours, which is what makes the economics work. The FDEs are agentic AI specialists, not generalists rotated onto an AI account. And the contract names a delivery date the firm is financially exposed to.
- Forward-deployed engineer (FDE)
- An FDE embedded with your team for the length of the engagement, holding the architecture and cutover calls, and personally accountable for the date in the contract.
- Fixed-date delivery
- A commercial model in which the ship date appears in the signed statement of work, and Mactores absorbs the cost of any delay Mactores causes.
- Fixed-fee
- One engagement price, set once scoping is complete and confirmed before build starts, as opposed to open-ended time-and-materials billing.
- App-to-agent conversion
- Converting an existing software product into an agent, either by exposing its functionality as a callable tool for other agents, or by replacing its form-and-click interface with a conversational, intent-driven one.
- Evaluation harness
- Automated tooling that scores an agent against the acceptance thresholds named in the build phase above, scored against live or production-representative traffic before the cutover window opens.
- Retrieval decay
- The silent degradation of an agent's answer quality as source documents change, embeddings go stale, or the corpus grows past what the original index was tuned for, without any change to the underlying model.
- NIST AI Risk Management Framework
- A voluntary framework published by the US National Institute of Standards and Technology for structuring AI risk categorization, governance, and monitoring, used to document agent risk posture during scoping.
The commitment in full
Read exactly what Mactores is on the hook for.
The ship date, the total fee, and who carries the cost if that date moves — set out in contract language you can check line by line.
Which AWS Credentials Back This Work?
Mactores holds the AWS Agentic AI Specialization, granted specifically for agent-based delivery and a different bar from the general consulting status most AI vendors carry. Alongside it, seven AWS Consulting Competencies (Migration and Modernization, DevOps, Data and Analytics, Machine Learning, AI Services, Healthcare, and Manufacturing and Industrial Services) and seventeen AWS Service Validations across data, ML/AI, and infrastructure.
Mactores is an AWS Premier Tier Services Partner. Every one of these is granted after an AWS technical review, which makes this the one set of claims on the page a buyer can verify without taking our word for it.
- Specialization
- AWS Agentic AI Specialization
- Partner tier
- AWS Premier Tier Services
- Service validations
- 17 Service Validations
- Team
- 200+ AWS-certified FDEs
- Building on AWS
- 18 years
7 Consulting Competencies
- Migration and Modernization
- DevOps
- Data and Analytics
- Machine Learning
- AI Services
- Healthcare
- Manufacturing and Industrial Services
Verified by AWS
Every credential here is granted by AWS, so you can check it without us.
The tier, the specialization and every competency are listed on our partnership page and in the AWS Partner Solutions Finder.
FAQ
What exactly does "fixed-date, fixed-fee" mean?
How long does an engagement typically take?
Can our own team maintain the agent once hypercare ends?
How do you handle data residency requirements?
Do you support regulated industries?
What happens to the pilot environment after cutover?
Are we locked into Mactores after this ships?
Who owns the code and configuration built during the engagement?
Will we pay what this page implies?
Ask us directly
Holding a question this page didn't answer? That is the one worth a call.
Thirty minutes with the forward-deployed engineer who would run the engagement.