2025년에 AI 대행사 런칭 및 확장: 실용적인 가이드

Adopt a narrow AI services focus and build a repeatable production concept within months. If a founder knows the constraints, the baseline for pricing and delivery becomes clear, and you can craft a value proposition that resonates with those clients seeking practical outcomes. Write a one-page brief that describes the problem, the data needs, and the measurable impact.

Define a crisp offering and prove it with 3 pilots to differentiate and accelerate client acquisition. Build a baseline methodology, focusing on a small set of industries, data partners, and outcomes. The creatives you produce–case studies, briefs, and dashboards–should clearly demonstrate how your production-grade solutions reduce cycle time and risk. Set a monthly target to write at least one client-ready case study and one template for SOWs.

Map acquisition channels that align with your niche and allocate 40% of early budget to paid experiments. Use production-grade tools and execution routines to deliver fast wins. We can replace outdated processes with advanced solutions to reduce cycle times and raise client satisfaction. Track wins for at least 6 months to confirm repeatability and build cash flow that saves you months of negotiating back-and-forth. This gives teams a clear path to growth that is possible with disciplined spend and a tight scope.

Assemble a lean team and partner with freelance creatives to extend capabilities while keeping control tight. Target a core group of 2–3 senior strategists and 4–6 designers who can deliver client-ready assets. Each month, write a plan with milestones and the projected impact on revenue, plus notes on processes that are saved annually through automation.

Set a 12-month growth plan with clear milestones: client base, repeat engagements, and gross margin. Use a simple baseline dashboard to measure win rate, acquisition efficiency, and monthly revenue. The plan should show what expansion is possible, with a realistic runway of 6–9 months to break even for a small studio, assuming you frontload 2 high-impact projects and reinvest the profits in marketing and tooling.

Document your concept in a concise playbook that can be shared with investors, partners and clients. Maintain a living library of templates for scoping, data requests, and delivery dashboards. A focused approach reduces confusion, accelerates onboarding, and creates predictable outcomes.

90-Day Launch Roadmap for an AI Agency (2025)

Initiate a 90-day cadence with a proprietary service package and three client-focused campaigns to prove ROI. The onboarding allocates a half-day workshop, a crisp discovery checklist, and a two-week pilot window to validate value. Once ROI is demonstrated, extend the engagement to a broader client base.

Days 1–14: planning, partnering, and staffing. Finalize target niches, appoint a specialist lead, assemble a freelancer pool for data ops and model tuning, and define integration points with client systems. Establish an integrated tech stack and a data governance policy, such as CRM, ERP, and security tooling.

Days 15–30: build reusable templates and workflows. Create repetitive workflows for data ingestion, model prompts, and reporting; implement command-driven automation to reduce manual steps and help teams perform tasks more efficiently. Draft training materials and a schedule to train client teams and internal members using sandbox environments.

Days 31–45: drive adoption and collect approval. Run two pilot campaigns with live data; measure uplift in key metrics; secure early adoption from stakeholders; formalize an approval process to greenlight expanded workstreams.

Days 46–60: optimize and expand. Refine models, tighten SLAs, consolidate results into a single package for sharing with prospects; strengthen partnering with vendors and clients; ensure the specialist leads the handoff to client teams; expanding into adjacent service areas to accelerate expansion.

Days 61–75: process maturation. Document entirely repeatable playbooks, reduce touchpoints, and embed integrated dashboards. Equip the team with training on new capabilities; implement a center of excellence to sustain growth.

Days 76–90: grow and plan next quarter. Formalize expansion into additional verticals; extend advisory campaigns; build a long-term plan for onboarding, adoption, and client success; align with the partner ecosystem to accelerate growth.

Validate a profitable niche: interview script, signal metrics, and 10-customer test

Recommendation: run ten buyer interviews within the target segment to prove willingness to pay and to define the most lucrative sub-niche before committing to larger production efforts.

Interview script

Opening and disclosures: state the objective, ask for permission to record, and confirm data usage. Keep it focused and brief so the conversation stays productive.
Discovery questions: identify the largest pain points, quantify impact in revenue or time saved, and map the current process to a 1–5 severity scale.
Value demonstration: describe a minimal service line that addresses the top pain, share a hypothetical ROI, and ask if the outcome would justify an investment.
Willingness to pay and procurement: surface budget range, decision authority, and whether the buyer prefers a pilot, a light engagement, or a full project.
Closing and next steps: propose a 2-week pilot or a paid discovery sprint, and schedule a bookable follow-up to review results.

Signal metrics and scoring

Problem clarity score: how well the respondent articulates the issue (0–4).
Impact potential: estimated impact on revenue or efficiency (0–4).
Urgency to act: willingness to move now (0–4).
Willingness to pay (WTP): stated budget or price tolerance (0–4).
Buying authority: ability to sign off or influence the decision (0–2).
Feasibility of delivery: alignment with current systems and constraints (0–3).
Disclosures and transparency: completeness of disclosures about scope and limitations (0–2).
Total scoring: sum of the above (0–19). Set a pass threshold (e.g., 12+ on at least 6 of 10 interviews) to proceed.

Implementing the scoring within a shared system like airtable creates a single view for all respondents, speeds collaboration, and preserves a clear roas lens for decisions. Use the leaderboard to identify the leader sub-niche and to compare against traditional benchmarks.

10-customer test plan

Definition and focus: define the target buyer profile, prioritizing the largest addressable opportunities and the most straightforward adoption paths. This initial scope change helps establish a credible baseline.
Screening and booking: pre-screen 25–40 prospects, then book 10 detailed interviews with qualified candidates. Don’t schedule more than your capacity to perform deep dives.
Interview cadence: complete interviews within one week; synthesize insights overnight to keep momentum and adapt the approach quickly.
Data capture: store each interview in airtable with a standard template for questions, answers, and scores. Link disclosures and context as you go.
Analysis and scoring: compute the signal score for each respondent, aggregate results, and compare against the pass threshold.
Go/no-go criteria: require a clear winner sub-niche in terms of WTP and feasibility; validate at least one concrete pilot option with a defined ROAS expectation.
Pilot design: for the chosen focus, craft a production-ready but minimal pilot package, including deliverables, timelines, and success metrics.
Disclosures and ethics: document all data usage, privacy commitments, and client expectations to avoid later disputes.
Leadership and ownership: assign a single leader to drive the test, with defined milestones and weekly check-ins to keep the team aligned.
Review and scale: if the pilot meets ROAS and client feedback targets, establish a repeatable blueprint and begin onboarding additional organizations.

Tools and speed considerations

Airtable acts as the central scoring and discovery ledger, enabling faster collaboration and transparent ROAS tracking.
Production-ready materials should be lightweight yet robust, so you can implement quickly without overbuilding.
Focus on a clear definition of success: a measurable outcome, a credible buyer signal, and a path to a paid pilot.
Be mindful of disclosures and governance when sharing sensitive data with prospects and clients.

Common traps to avoid

Overpromising results before the pilot proves impact.
Ignoring procurement realities or budget constraints in the signal metrics.
Expanding scope too early; stay focused on the initial, high-potential niche.
Relying on anecdotes; rely on the 10-customer test data to guide decisions.

Next steps

Finalize the interview script and screening criteria within 48 hours.
Launch the 25–40 outreach push and start booking the 10 deep-dive conversations.
Populate airtable with response data, run the scoring, and identify the strongest sub-niche for a paid pilot.
Prepare disclosures and a clean pilot proposal to present to the leading buyer(s) who meet the thresholds.

Legal setup and data compliance: business registration, contracts, and GDPR/CCPA checklist

Immediate recommendation: register a formal business entity and open a dedicated bank account inside your corporate structure to clarify ownership, taxes, and liability while you scale project-based engagements.

Choose the entity type with a longer-term vision; strategically select options such as LLC or equivalent, which impacts governance, funding avenues, and risk posture. Document the ownership and decision rights with a clear number of stakeholders, and align this structure with your hundreds of client relationships.

Contracts should build a standard library of SOWs, NDAs, and data processing agreements (DPAs); incorporate data handling, subprocessor authorization, breach notification timelines, and termination rights so performance remains compliant across all projects. Ensure each deal with developers and freelancer teams follows the same template to avoid gaps when partnering.

GDPR/CCPA checklist: map data flows inside your operations, classify personal data types, and document lawful bases; implement data subject rights templates, retention schedules, and data minimization rules; establish SCCs for cross-border transfers where applicable.

Governance framework: designate a data protection lead or governance owner, define responsibilities, and implement an accountability model with regular analysis of controls; schedule monthly reviews to keep posture aligned with evolving requirements and client expectations.

Security and access controls: enforce least privilege, multi-factor authentication, encryption in transit and at rest, and secure backups; maintain an incident response manual and run practice drills to reduce response times; log retention should be aligned with regulatory needs and internal policy.

Data subject requests: prepare templates to respond instantly to access, deletion, and portability requests; track requests and outcomes in a centralized system, capable of handling hundreds of inquiries without slowing operations.

Vendor and partnering strategy: require DPAs with all processors, include data-transfer mechanisms when working with developers, freelancers, and agencies, and maintain a vendor risk registry; conduct deeper due diligence on data handling practices before any onboarding.

Project-based engagements: embed privacy and security clauses in every SOW; implement standardized onboarding playbooks and a clear order flow to avoid solely ad hoc agreements; this approach builds predictable risk profiles and faster approvals.

Operational setup for talent: provide a manual onboarding toolkit for freelancers and internal staff; deliver privacy and compliance training as part of the onboarding cycle and require developers to acknowledge data handling rules during the first meeting.

Implementation timetable: target a 60–90 day window to complete core registrations, DPAs, and essential controls; track monthly progress against a privacy breach risk score and adjust budgets to protect profits while maintaining compliance.

Documentation discipline: keep a governance binder with data inventories, processing logs, and decision records; update records after any policy change or new processing activity to maintain an auditable trail inside the organization.

Meeting cadence: establish quarterly reviews with clients and internal stakeholders to align on risk, privacy posture, and long-term vision; use these sessions to surface improvement opportunities and refine your contracts and governance.

Scaling perspective: with hundreds of client relationships, automation and standardized templates are essential; partnering with other agencies can accelerate governance maturity while keeping sole responsibility for compliance within your leadership team.

Technical MVP plan: model choice, minimal dataset pipeline, and deployment stack

Use a 13B Llama 3 backbone with 8-bit quantization and LoRA adapters for instruction-following, hosted on a single high-end GPU. This delivers predictable latency, cost efficiency, and fast iteration for the MVP. Then spin up a minimal API to expose the model and a prompt-template library to keep outputs aligned with branding and calls to action.

Model choice should balance capability and risk: prioritize open, well-documented checkpoints in the 7–13B range (Vicuna, Mistral, or Llama 3 variants) with lightweight adapters, so you can iterate on instruction quality without breaking the budget. Compare major metrics: perplexity, alignment scores, hallucination rates, and latency under load. Use a simple evaluation suite and a small sanity rubric to approve releases, while keeping a safety guardrail layer in front of any live prompts. Uncover gaps with a quick qualitative test run and a written feedback loop from a research or product partner, then decide on a single backbone for the next sprint, Miguel’s team included.

Minimal dataset pipeline: source domain prompts and ideal responses from internal knowledge bases, then augment with high-signal synthetic prompts that mirror real-world casting and inquiries. Keep the dataset compact: 200–500 gold prompts for quick drift checks and 1,000–2,000 additional prompts for resilience. Deduplicate, normalize formatting, remove PII, and version data with lightweight tooling. Store as JSONL with fields for prompt, completion, category, and a confidence tag; annotate samples with a specific privacy note and a disclosure line for clients. Maintain a small, written policy doc to govern data usage and approvals for new data additions, then codify the next iteration cycle.

Deployment stack: containerize the model with Docker, run a FastAPI API, and place the service behind a small, scalable inference runtime (TorchServe or Triton Inference Server). Host the artifacts on a cloud VM or managed instance with autoscaling, and layer Redis for caching of frequent prompts. Use S3-compatible storage for model artifacts and dataset versions; implement a lightweight CI/CD (GitHub Actions) to push approved model winds and prompt templates. For delivery, expose a stable endpoint with versioned routes and a simple distribution policy to manage rollouts; ensure quick rollback in case of issues and maintain a concise disclosure note for clients. Combined observability should track latency, error rate, and throughput, with daily scores to guide the next release.

Operational and governance points: establish a clear approval threshold for any data or model change, and keep a compact deck for stakeholders. Focus on cost per 1K tokens, latency targets, and safety checks; set minimum benchmarks to sell the MVP as a practical capability rather than a speculative build. Craft branding-friendly outputs with consistent tone and structure, so every call reads like a written, professional response across channels. Measure relevance and accuracy against a small, representative client set, then publish a short research brief to inform the industry feel and establish credibility in the major segments of the market.

Team notes and next steps: document the approach in a lean technical deck, assign owners, and align on a single, auditable data path. Keep the distribution plan transparent and ready for client approval, with a minimal disclosure section and a fallback option if data constraints arise. The deck should include a quick risk assessment, a cost forecast, and a roadmap from MVP to a scalable platform, with Miguel leading the technical review and ensuring the points align with the company’s branding and strategy plans.

Offer design and pricing templates: pilot scope, retainer vs outcome-based examples, and proposal template

Recommend a three-part pricing framework: pilot scope with clearly defined deliverables, a minimal retainer for ongoing work, and an outcome-based option tied to measurable results.

Pilot scope design: duration 4–6 weeks with a tight boundary around 2–3 core use cases. Deliverables include a discovery report with a data map, a proof-of-concept model or playbook, and an evaluation plan with defined success criteria and acceptance tests. Set a simple transition plan to the next phase and a formal handoff to partners so responses from their team are captured and tracked in the toolkit. Use non-technical framing to keep expectations clear across stakeholders and maintain focus on business impact.

Retainer pricing example: for ongoing engagement, structure around 24–40 hours per week across sprints, with a monthly price that reflects scope and maturity of the project. Typical ranges run from $8,000 to $15,000 per month, including biweekly calls, backlog grooming, dashboards, model monitoring, and regular optimistic learnings that push evolution. Include deliverables such as iterative improvements, governance playbooks, and knowledge transfer; payments on a monthly order in advance; and a 30-day transition window if expanding the scope.

Outcome-based example: base governance retainer plus a success fee tied to quantified uplift or savings. Define the uplift metric up front (revenue lift, cost reduction, or efficiency gain) and set a measurement window (commonly 90–120 days). Typical structure: a modest monthly base (e.g., $5,000–$8,000) plus a negotiated percentage of the measured benefit (often 12–25%). Ensure baseline data, verification rights, and clear exclusions to keep pricing fair for both sides and to avoid disputes in responses or changes. This model aligns maturity with value and reduces risk for their organization while expanding the provider’s opportunities.

Decision framework across types of buyers: across partners who want predictable costs, the retainer path offers steadiness; for those with aggressive growth goals or clear metrics, the outcome-based route can deliver better alignment. When clients lack internal data maturity, frame the engagement around delivery of specific deliverables and a transparent transition to full capability, framing risk in terms of achievable milestones rather than broad promises. Keep framing concrete and accessible, and use a marketing-friendly narrative that highlights their gains while acknowledging constraints. Could include a quick information sheet that summarizes costs, milestones, and expected responses to common questions, easing the transition for non-technical buyers.

제안서 템플릿: 섹션 순서는 간결하고 비즈니스 중심이어야 합니다. 다음을 포함하세요: 1) 목표 및 예상 결과가 포함된 경영진 요약; 2) 고객 상황 및 원하는 영향; 3) 제공물 및 승인 기준이 포함된 파일럿 범위; 4) 접근 방식 및 프로젝트 구조(부분, 이정표, 및 거버넌스); 5) 가격 및 지불 조건(정액제, 성과 기반, 또는 혼합형); 6) 역할, 책임, 및 일정; 7) 위험, 종속성, 및 변경 관리 프로세스; 8) 성공 지표 및 검증 계획; 9) 변경 주문 프로세스 및 해지 조건; 10) 다음 단계 및 행동 촉구 사항. 자주 묻는 질문에 대한 샘플 응답 시트, 샘플 킥오프 계획, 및 각 제공물 뒤에 있는 개념에 대한 간략한 설명이 포함된 부록을 추가하여 파트너가 작업 방식을 이해하도록 돕습니다. 제공물 및 일치성을 강조하는 최소한의 깔끔한 레이아웃을 사용하고, 팀이 빠르게 사용자 정의할 수 있도록 편집 가능한 버전을 제공합니다. 이러한 접근 방식은 정보를 명확하게 유지하고, 고객이 조직 전체에서 가치를 인식하도록 돕고, 이해 관계자가 신뢰할 수 있는 안정적인 프레임워크를 제공합니다.

최초 10명의 고객을 위한 Go-to-market 플레이북: 도달 범위 확보 시퀀스, 데모 키트, 전환 KPI

4주 안에 3단계 아웃리치 시퀀스와 발표 준비된 데모 키트를 사용하여 10개의 목표 데모를 예약했습니다.

타겟 고객층을 정의합니다. SaaS, 전자 상거래, 전문 서비스 회사의 미드마켓 운영, 마케팅, 제품 리더를 대상으로 합니다. 귀사의 제품을 파일럿 프로젝트를 가속화하고 위험을 줄이며 몇 주 안에 가치를 제공하는 최첨단 자산으로 포지셔닝하세요. 산업, 회사 규모, 역할에 대한 정확한 타겟팅을 사용하여 모든 접점에서 관련성을 높이세요.

Outreach sequence: implement a three-phase cadence over 10 days. Phase 1 is a concise intro email with a 2‑line problem statement and a CTA to book a 15-minute walkthrough. Phase 2 is a LinkedIn touch or social post with a quick case snippet and a link to the calendar. Phase 3 is a value-driven follow-up with a one-page ROI snapshot and a final invitation to review the live demo kit. Youll optimize subject lines and messages for clarity, and collect replies to move them straight into the calendar flow to streamline scheduling.

데모 키트 구성 요소: 빠른 시청을 위한 최적화된 8~12페이지 프레젠테이션, 1페이지 ROI 계산기, 60초 오프닝 비디오, 반복 가능한 흐름을 갖춘 실시간 데모 스크립트, 측정 가능한 결과가 있는 2개의 짧은 사례 연구, 번들 가격 범위를 갖춘 서비스 카탈로그, 구현 타임라인, 그리고 다음 단계에 대한 티저. 속도, 신뢰성, 그리고 측정 가능한 영향을 보여주는 시각 자료를 포함하십시오. 비동기 시청을 위한 녹화된 버전을 제공하고, 통화 중 마찰을 줄이기 위한 경량 Q&A 시트를 제공하십시오.

전환 KPI: 열람률, 회신율, 데모 예약율, 출석률, 제안 수락률, 그리고 계약 완료까지의 시간을 추적합니다. 목표 벤치마크: 열람률 25–40%, 회신율 8–18%, 회신에서 예약된 데모 15–30%, 출석률 70–85%, 제안 성공률 30–50%, 파일럿 프로그램의 평균 판매 주기 14–28일, CAC는 첫 해 ARR의 20–30% 미만입니다. 처음 10명의 고객의 경우, 50–70%의 아웃리치 응답을 캘린더 예약으로 수집하고 명확한 의제와 사전 읽기가 포함된 캘린더 초대장을 보내 60–80%의 데모 출석률을 유지하는 것을 목표로 합니다.

실전 연습에서는 에이전시, 귀사, 프리랜서, 그리고 제공업체들이 제공할 수 있는 반복 가능한 자산으로 역량을 구조화합니다. 역할과 책임을 매핑합니다. 핵심 계정 담당자, 메시징 컨설턴트, 시각 자료 담당 디자이너, 그리고 런-오브-쇼 스크립트가 있는 데모 호스트 또는 프리랜서를 구성합니다. 자원에는 라이브 데모 키트, CRM 준비 완료 시퀀스, 짧은 사례 연구 라이브러리, 그리고 데이터가 들어오면 팀이 빠르게 업데이트할 수 있는 ROI 모델이 포함됩니다. 이러한 접근 방식은 프로세스를 간단하고, 확장 가능하게 하며, 고객에게 실제적인 성과를 창출할 수 있도록 합니다.

데이터 수집 및 반복: 모든 홍보 및 데모에서 피드백을 수집하고, 메시지 성과를 추적하며, 실현된 수치를 사용하여 ROI 계산기를 업데이트합니다. 내부 데이터를 활용하여 타겟팅을 개선하고, 가치 제안을 조정하며, 데모 키트 시각 자료를 향상시킵니다. 목표는 각 상호 작용을 명확한 가치 정의와 다음 단계로 이어지는 더 빠른 경로로 전환하는 것입니다.

운영 일정: 0~2일차 ICP, 자산, 캘린더 링크 준비; 3~7일차 홍보 실행 및 응답 수집; 8~10일차 데모 일정 예약 및 데모 키트 전달; 11~14일차 데모 실행, 성과 포착 및 제안서 작성 시작. 프로세스를 단순하게 유지하고, 신속하게 조치를 취하며, 다양한 서비스에서 성공적인 패턴을 재사용하여 가능한 최대 이익을 얻으십시오.