How we work.
Three phases. Measurable results in ninety days. Complete ownership by the end. This is how every SynthesisArc engagement runs, and the page below describes each phase in operational detail.
2 weeks
Find
INSIGHTS assessment. Operations mapped, opportunities ranked, dollar figures attached.
90 days
Fix
Deterministic AI deployed around your highest-impact workflows. Measurable results, or you do not pay.
Forever
Future-Proof
Complete transfer. Full documentation, hands-on training. Your team runs the systems. No retainer trap.
Why three phases
The methodology is shaped by how enterprise AI actually fails.
Most AI engagement models compress diagnosis and deployment into a single workstream. That sounds efficient. It is also the reason MIT NANDA's 2025 study found 95 percent of enterprise GenAI pilots fail to deliver measurable financial impact. When you compress diagnosis into deployment, you end up deploying into operations that are not ready, against problems that were not the highest-value targets, with no agreed metric for what success looks like.
Find, Fix, Future-Proof is structured so each phase has a falsifiable output before the next phase starts. Find produces a dollar-value roadmap. If we cannot put numbers on the opportunities, you do not have to keep going. Fix produces measurable operational outcomes against a baseline you set. If we miss the numbers, you do not pay. Future-Proof produces a working system inside your team. If the documentation and training are not sufficient to let your people operate the system without us, the engagement is not complete.
The methodology is documented end-to-end, audited against industry benchmarks, and built on the same Operational Intelligence discipline we deploy for clients. BCG's 2025 study of 1,250 CXOs across 59 countries found that only 4 to 5 percent of enterprises capture full AI value, while another 22 percent capture substantial gains. The methodology below is designed to operate in that top tier.
The problem we solve
95% of enterprise AI pilots fail.
Per MIT NANDA's 2025 study. The cause is rarely the model itself. It is the work around the model that goes missing. Find, Fix, Future-Proof is that work.
Phase one
Find where AI moves the needle.
You cannot automate what you have not measured. Every engagement starts with the INSIGHTS assessment: a two-week diagnostic that maps your operations, identifies your three highest-impact AI opportunities, and puts a dollar figure on each one. The deliverable is a roadmap you can defend to a board.
Operations mapping
We interview executives, operational leaders, and frontline staff. We map every workflow that matters: process steps, decision points, integration boundaries, current cycle times, current error rates.
Seven-dimension readiness scoring
Strategic clarity, data foundation, workflow maturity, infrastructure, governance, team capability, change capacity. Each dimension scored against what AI actually requires to perform in production.
Dollar-value roadmap
Every opportunity gets a dollar figure. Every recommendation has a timeline and a risk profile. You walk out with numbers a CFO can stress-test, not a slide deck of generic recommendations.
Inside Phase one
How the two weeks are spent.
The two-week window is divided into three working blocks. Days 1 through 4 are interviews and document collection. We meet your executive sponsor, your operational leaders, and the frontline staff who actually run the workflows in question. We collect process documentation, sample data, current systems inventory, and (when relevant) prior consulting deliverables that did not produce results, because the failure pattern is itself diagnostic.
Days 5 through 9 are analysis and scoring. We score your operation on the seven readiness dimensions, with evidence backing each score. Strategic clarity is scored against whether the C-suite has alignment on what AI is supposed to do. Data foundation is scored against whether the data needed for the workflow exists, is accessible, and is clean enough to drive automated decisions. Workflow maturity is scored against whether the process is consistent enough to be automated at all, because BARC's 2025 research identified data quality as the top obstacle for AI initiatives at 44 percent of organizations, up from 19 percent a year earlier. Governance is scored against the NIST AI Risk Management Framework and applicable industry regulations.
Days 10 through 14 are roadmap construction. We rank the three highest-impact AI opportunities by expected dollar value, time-to-value, technical risk, and organizational risk. Each opportunity has an estimated cost range, an estimated benefit range, an implementation timeline, and a list of prerequisite work. The output is a written executive summary plus an annotated technical roadmap. You can take that document to a CFO and they can model it.
INSIGHTS exists because of a pattern we kept seeing in clients who had already spent money on AI without results. The deployment was sophisticated. The deployment target was wrong. Or the target was right but the data layer underneath was not ready. Or the data was ready but nobody on the operating team had been brought along through the change. Diagnosis prevents all three failure modes by surfacing them before the build budget gets spent. Read the full method on the INSIGHTS product page.
What you walk away with
Phase one timeline
2 weeks.
Not six months of consulting. Two weeks to know exactly where AI should apply in your operations, what each opportunity is worth, and what stands in the way.
Phase two
Fix the workflows that matter.
Now that we know what to build, we build it. Deterministic AI systems deployed around your three highest-impact workflows. Measurable results in ninety days, or you do not pay. The architecture is PRISM, the governance layer is Claude Guard, and the metrics are agreed before the build starts.
Deterministic AI deployment
Your operations need AI that produces the same output for the same input. Every time. We build on PRISM, the intelligence architecture engineered for reproducibility, with full audit trails on every decision.
90-day results commitment
Measurable operational outcomes within ninety days of build start, or you do not pay. No other AI consulting firm makes this promise. The metrics are agreed in writing before a line of code is shipped.
Measured against your baseline
Cost per transaction, error rate, throughput, time to decision. The baselines come from Phase one. You grade the exam, not us. The metrics are observable in your own systems.
Inside Phase two
How the ninety days are spent.
The ninety-day build is divided into three thirty-day sprints. Sprint one is architecture and integration: we instrument the workflow against your current systems, wire the deterministic AI components on PRISM, and establish the governance perimeter on Claude Guard. Sprint two is operational deployment: we run the workflow in shadow mode against your live operation, comparing outputs, surfacing exceptions, and tuning until the determinism holds across the full input distribution. Sprint three is cutover and validation: the system goes live, the baselines we agreed in Phase one are measured against actuals, and a written validation report is produced.
Determinism is the architectural commitment underneath all of this. A probabilistic system can pass a demo and fail in production, because demos sample the easy cases. A deterministic system produces the same output for the same input, with the same audit trail, on the millionth run as on the first. PRISM enforces this at the architecture level rather than depending on prompt-engineering tricks, which is what makes a 90-day results commitment financially sane for us to offer. Read the full architecture writeup on the PRISM product page.
Governance is built into the build, not appended to it. Claude Guard enforces the policy perimeter at runtime: which data the model can see, which actions it can take, which outputs require human review, which records must be retained for audit. The EU AI Act, Regulation (EU) 2024/1689, brings Annex III high-risk obligations into force on August 2, 2026, with penalties up to 35 million euros or 7 percent of worldwide turnover. SEC AI disclosure rules are tightening in parallel. Building governance as engineering, not as a separate policy document, is the only way a deployment survives both regimes without a remediation project.
Change management runs in parallel with the technical build. Prosci's 12th-edition research on best practices in change management shows projects with excellent change management meet or exceed objectives 88 percent of the time, compared with 13 percent for projects with poor change management. That is a 6.8x multiplier on success probability, and it is the reason we run executive and operator enablement during the build, not after the cutover.
The metrics we measure against
Cost
Per transaction, before vs after
Error
Rate of operational mistakes
Speed
Throughput and time to decision
ROI
Quantified against the Phase one baseline
Phase two timeline
90 days.
Measurable results, or you do not pay. This is the commitment no other AI consulting firm makes. It is possible because Phase one already cleared the diagnostic risk.
Phase three
Your team owns it.
The systems we build belong to you. Full documentation, hands-on training, complete handoff. No retainer trap. No vendor lock-in. Your team runs the systems long after we leave, and the audit trail is intact whether we are still in the room or not.
Complete documentation
Every workflow, every decision rule, every integration documented at the level your team can actually maintain. Not a summary. The operational manual a successor engineer could pick up cold.
Hands-on training
Your team learns the systems by running them. We train side-by-side until the transfer is verifiable. They do not read a manual. They take the wheel under observation, then on their own.
AI sovereignty
You own the data, the models, the workflows, the outcomes. Nothing is held hostage in a vendor environment. When we leave, the capability stays inside your organization.
Inside Phase three
How the handoff is engineered.
The transfer is treated as an engineering deliverable, not an afterthought. Documentation is generated as the system is built, not retroactively. Every decision rule, every model invocation, every governance check, every integration point is recorded in a runbook structured for the engineer who will inherit it. The test is simple: can a competent operator on your team, who was not in the Phase two build sessions, run the system end-to-end using only the runbook. If the answer is no, the runbook is not done.
Training is graduated and observed. Week one of the handoff window is shadowing: your team watches us run the production system. Week two is co-piloting: your team runs the system with us in the room. Week three is solo operation: your team runs the system without us, and we observe and capture issues. Week four is the validation review: any gaps surfaced in week three are closed, and the engagement closes against a written acceptance test. If a gap stays open, the engagement stays open.
Sovereignty is the structural commitment. Every system, every model, every workflow is transferred with full IP rights to your organization. The deployment lives in your environment, on your infrastructure, with your data perimeter intact. If you want to take the system to a different consultancy six months later, you can, because nothing about the architecture depends on us being in the loop. This is the operational definition of AI sovereignty, and it is the structural reason our engagements end cleanly rather than turning into perpetual retainers.
Why this matters
"Most consulting firms are built on recurring revenue, which means they are built to keep you dependent. SynthesisArc is built on repeat business from solved problems."
This is the difference between a consulting relationship and a consulting trap. Our engagements end. Our client relationships continue, because the next problem brings them back on purpose, not by lock-in.
How we measure success
The end-state test.
A SynthesisArc engagement is successful when four conditions are met, and only when all four are met.
The roadmap is defensible. The Phase one INSIGHTS deliverable holds up under board scrutiny, with dollar figures a CFO can model and a risk profile a CRO can underwrite.
The numbers are met. The Phase two cutover delivers against the baselines agreed in Phase one. Cost reduced, error rate down, throughput up, ROI positive against an independent reading of your own systems.
The handoff is complete. The Phase three validation review closes with your team operating the system independently, with documentation that survives staff turnover, and with no open dependency on SynthesisArc for day-to-day operation.
The compliance posture is intact. The deployment is audit-ready against the EU AI Act, NIST AI RMF, and applicable industry frameworks, with a Claude Guard policy perimeter that holds without manual oversight.
If any one of those four fails, the engagement is not closed. The work on this page is what we sell. The four-part end-state test is what we deliver against. Read the case studies in Showcases, or the methodology essays in the Field Notes library.
The complete picture
Find. Fix. Future-Proof.
Two weeks to know. Ninety days to results. Complete ownership at the end. That is how we work. Every engagement. No exceptions.
Every engagement begins here.
The INSIGHTS assessment is the door. Two weeks. Dollar-value roadmap. No commitment beyond the conversation.