Generative AI Consulting for the Enterprise

Generative AI, shipped to production

Generative AI consulting is not a whitepaper exercise. Enterprise buyers now have clear expectations: a running application in a defined workflow, grounded in the client's proprietary content, with governance that passes a compliance review, at a cost that makes sense against the value. The firms that still produce slideware have been priced out of serious engagements.

Thoughtwave's generative AI practice ships production applications. Every engagement ends with a running system, a documented architecture, a governance posture, and an evaluation pipeline that keeps the system honest as it ages.

The workloads we deliver

Four patterns cover most engagements:

RAG-grounded knowledge assistants

The model answers questions by retrieving relevant content from the client's proprietary sources — product documentation, regulatory guidance, prior resolved cases, engineering specs — and generating an answer with citations. Used across customer service (TWSS CS Agent pattern), advisory work (TWSS Finance AI/ML pattern), and internal engineering and operations teams.

Drafting copilots

Embedded in CRM, email, documents, or internal tools. The user writes a prompt or describes an intent; the model drafts a response; the user edits and sends. Tone policy is a single prompt, updated in one place, applied across every draft. Clients adopt this for external client communication (email, CRM cases), internal communication (reports, briefs), and technical work (code, configurations, content).

Structured extraction pipelines

Unstructured input (PDF, email, form, scan) enters; structured output (JSON, database row) leaves. The model does the extraction; validation rules catch exceptions; downstream systems receive clean records. TWSS AI Invoice Automation is a production example — email-to-Tyler-Munis posting with zero manual entry on clean cases.

Domain-tuned assistants

A narrower assistant tuned to a specific domain or role — field technician support, analyst research, sales enablement, SAP exception handling. Often paired with tool use that starts to push toward agentic territory.

Our engagement shape

Standard pilot arc is 6-10 weeks:

• Weeks 1-2. Discovery and workload selection. Identify the first production workflow; inventory knowledge sources; document compliance and audit requirements; run the vendor evaluation.
• Weeks 3-6. Build. Ingest and chunk the knowledge sources; stand up the retrieval layer; prompt-engineer the workflow; wire the integration with the CRM, inbox, or internal tool the users actually work in.
• Weeks 7-8. Evaluate. Run the application in shadow mode against live traffic or a representative test set. Tune retrieval, grounding, and response quality. Measure against pre-agreed success criteria.
• Weeks 9-10. Ship. Cut over to production with monitoring, audit, and the feedback loop to keep improving.

Subsequent workloads on the same platform ship in 3-6 weeks each.

The reference architecture

Four components on every deployment:

1. Knowledge layer. Source ingestion, chunking, embedding, vector store (Postgres + pgvector as the default; dedicated engines at scale). Source freshness matters as much as retrieval quality.
2. Reasoning layer. Vendor-neutral model choice with runtime switchability. Scoped to the workload; tuned via prompt engineering, retrieval grounding, and — where needed — fine-tuning.
3. Integration layer. CRM, email, document system, or internal tool. The model appears inside the workflow the user is already in; no separate portal.
4. Governance layer. PII redaction, content safety, source citation surfacing, audit log, grounding evaluation, drift monitoring.

Self-hosted deployments for regulated workloads

For clients where data cannot flow to external vendors — HIPAA, GLBA, PCI-DSS, FedRAMP, specific contractual restrictions — we deploy on self-hosted infrastructure. The reference pattern is TWSS Commercial Credit AI: a 3-model ensemble (Qwen 2.5, Gemma 27B, Llama 3.3 70B) served via Ollama on client GPUs, zero external API calls, full audit per transaction, MBE/GSA-procurable.

For the decision framework on self-hosted versus cloud, see the case for self-hosted AI.

Reference engagements

• TWSS CS Agent for a retail safety solutions company — 60% faster case response, full audit trail. Case study.
• TWSS Finance AI/ML for a wealth-advisory firm — FINRA/SEC-ready audit, compliance-aware retrieval. Case study.
• TWSS AI Invoice Automation for a Tyler Munis municipality — zero manual AP entry. Case study.

The accelerators portfolio has 24+ production solutions across customer service, AP automation, analytics, staffing, lending, and more.

Why Thoughtwave

• Production track record, not whitepapers.
• Vendor-neutral model selection; cloud or self-hosted per workload.
• Governance and audit built in from day one, not retrofitted.
• MBE and GSA-approved — procurement paths many AI vendors cannot meet.

For broader context, see our AI & Generative AI service. To start a conversation, book a consultation.