AI for Enterprise Services Firms

It depends on scope and integration depth. A focused single-workflow build (e.g. proposal automation or knowledge search) typically runs $30,000–$80,000; a broader rollout across multiple workflows with deep practice-system integration and firm-wide governance runs $80,000–$200,000+. We almost always start with a fixed-price PoC (typically $15,000–$25,000) that proves the hours saved on one workflow before you commit to more. Every engagement begins with a free discovery call to scope requirements and give you a realistic estimate — and an honest view of the likely ROI.

A focused PoC delivers a working tool on your real data in 4–6 weeks, with measured hours-saved on the target workflow. A production rollout of a single high-value capability typically takes 10–16 weeks including integration, confidentiality controls, and adoption support. The biggest variable is how accessible your data and systems are — well-documented APIs make integration fast, while legacy practice systems add connector time. We sequence delivery so you see working software and an early proof of value, not a long silence before a big-bang launch.

Confidentiality is the precondition, not an add-on. We enforce ethical walls and matter-level access so AI only sees the clients and matters a given user is cleared for — isolation between clients is enforced in the system, not left to the model's discretion, so a retrieval or copilot request can only surface what the requester is entitled to see. This mirrors the ethical walls your firm already maintains and is essential when you're juggling conflicts, confidential deals, or competing clients. Client data is never used to train public models; for the most sensitive work the system runs inside your tenant or fully self-hosted so data never leaves your perimeter; and every access and output is logged. We document exactly how the boundaries are enforced so your risk and compliance teams — and your clients — can verify it.

The best first use case is high-frequency, low-judgment, and unambiguously good for everyone — so the saved time is a clear win, not a threat. For most firms that's proposal/RFP drafting from past work, knowledge retrieval across the engagement archive, or automating non-billable overhead like status reports and timesheet narratives. Starting on overhead or write-off work sidesteps the billable-hour debate entirely. We help you pick deliberately in discovery, because the highest-ROI workflow is rarely the flashiest, and the wrong first choice is the most expensive mistake in the program.

Yes — this is one of the highest-ROI deployments for services firms. The system reads an incoming RFP or brief, extracts the requirements, retrieves your most relevant past proposals, case studies, bios, and pricing, and drafts a structured first response against your template — flagging gaps where information is missing. Your team edits and approves; the AI never submits anything autonomously. Typical saving is several hours per proposal, and because it pulls from your winning past work, the drafts start far closer to done than a blank page.

Yes. Integration with the systems your firm runs on is a core strength. We connect AI to document management (iManage, NetDocuments, SharePoint), CRM (Salesforce, HubSpot, Microsoft Dynamics), practice/PSA and time-and-billing systems, and ERPs via secure connectors or MCP. For older practice systems without clean APIs, we build structured wrappers so the AI uses your real data without forcing a risky migration. The AI also inherits your existing permission model rather than bypassing it, so integration doesn't open a new confidentiality gap.

Yes to both. We support deployments from your managed cloud tenant (AWS, Azure, GCP) to fully self-hosted environments running open-weight models where data never leaves your perimeter — so for firms with strict client or regulatory residency rules, confidential data is processed inside your boundary and we never need direct access to production data. And ownership is unconditional: your data, the system we build, the outputs, and all code are yours. We sign NDAs before any discovery conversation, never retain client data after an engagement, and never use your data to train models that benefit other clients — and we transfer knowledge deliberately (documentation, runbooks, pairing) so your team can own and operate the system rather than being locked into us.

For client-facing work this matters enormously, so we address it at several layers. Answers are grounded in your own source documents with citations (RAG), structured outputs are validated before use, low-confidence results are routed to human review, and we test against hallucination-specific cases before launch. Crucially, the workflow keeps a human in the loop — the AI produces a cited draft, your professional reviews and signs off. We agree an accuracy benchmark with you up front and monitor for drift in production, so quality is measured and bounded rather than assumed.

That's exactly what we build for. Because services firms handle their clients' confidential data, AI has to clear the same security bar your clients hold you to. We design to be compatible with the controls behind GDPR, SOC 2, ISO 27001, HIPAA, and emerging AI regulation — identity-first access, isolation, audit trails, and explainability — and we produce the architecture and control documentation your team needs to answer a client security questionnaire with evidence. A documented governance posture increasingly wins work; firms are citing it as a reason they're awarded contracts.

Not perfect data, and not a dedicated data team — but data readiness is the single most underestimated factor, so we assess it honestly in discovery. The trick is to choose a first use case whose data is already reasonably accessible, and to scope the project around the systems you actually have. Where data is messy or locked in scattered files, we either build the access layer as part of the work or steer you to a different first use case. A readiness gap is a reason to scope carefully, not a reason to wait.

Yes. Production AI needs ongoing attention, and we offer managed support and MLOps. Our standard engagement includes a period of included support after launch — monitoring, incident response, performance review, and minor adjustments. Beyond that, retainers cover model-drift monitoring, periodic re-evaluation against fresh data, cost optimization, feature additions, and integration maintenance as your practice systems change. Because models degrade quietly rather than failing loudly, this monitoring is what keeps quality and cost in check long after launch — and it's why we define operational ownership before go-live.