AI & Intelligent Systems
AI Solutions
From AI roadmap to systems in production: LLM integration, document intelligence, and custom AI platforms — engineered to pass your security review, your scale review, and your CFO.
Overview
Why it matters
Most enterprises don't have an AI model problem — they have an AI engineering problem. Foundation models are commoditizing fast; what separates the 12% of AI initiatives that reach production from the rest is everything around the model: data pipelines that feed it reliably, evaluation harnesses that quantify its behavior, guardrails that constrain it, and integration into the systems where work actually happens. That surrounding engineering is our practice.
Our AI Solutions practice covers the full arc: opportunity assessment and AI roadmapping, LLM integration into your existing platforms (ERP, CRM, service desks, document repositories), intelligent document processing for high-volume back offices, and custom AI applications built on your data. We are model-agnostic by design — GPT-5.x, Claude, Gemini, and self-hosted open-weight models via vLLM — and we select per use case on accuracy, latency, data-residency, and cost-per-task benchmarks, not vendor preference.
Every engagement ships with the operational spine enterprises require: evaluation suites run in CI, token-level cost telemetry, PII redaction and audit logging, and rollback plans. That discipline is why our AI systems survive their first security review — and their second year in production.
Business challenges
The problems this practice exists to solve
Pilots that never reach production
The proof-of-concept impressed the steering committee, then stalled for months in security review, data access negotiations, and integration debt. Industry-wide, most GenAI pilots never convert to production systems.
No defensible accuracy story
Leadership asks 'how often is it wrong?' and nobody can answer with a number. Without evaluation baselines and regression suites, every model update is a leap of faith — and legal won't sign off on faith.
Documents still processed by hand
Invoices, contracts, claims, KYC packets — thousands of pages a day keyed manually into downstream systems at $4–8 per document, with error rates that surface as costly exceptions weeks later.
Runaway and unpredictable AI spend
Token costs that looked trivial in the pilot compound at production volume. Without per-feature cost attribution, model routing, and caching strategy, CFOs see an invoice curve nobody forecast.
Our solution
How we engineer it
We start where most vendors end: with the measurement layer. Before any model touches your workflows, we build a golden evaluation dataset from your real cases and establish accuracy, latency, and cost baselines. Every architectural decision afterward — model choice, retrieval design, prompt strategy, fine-tuning — is justified against those numbers, so your team is never asked to approve something it cannot measure.
Then we engineer the system around the model: retrieval pipelines over your document stores, structured-output contracts validated at the boundary, human-in-the-loop review queues for low-confidence cases, and integration into the systems of record where the work lands. For document processing we combine layout-aware extraction models with LLM reasoning and confidence-based routing — straight-through processing for the clear cases, human review for the rest, and every correction feeding back into the evaluation set.
Finally, we operationalize: cost dashboards with per-feature attribution, drift monitoring, model fallback chains for provider outages, and a handover program that leaves your engineers owning the platform. You get a system, not a dependency.
Capabilities
What ai solutions covers
AI strategy & opportunity assessment
Two-to-four-week assessment that inventories candidate use cases, scores them on ROI and data readiness, and produces a sequenced roadmap your board can fund with confidence.
LLM integration & orchestration
Production integration of GPT, Claude, Gemini, and open-weight models into your platforms — with structured outputs, function calling, fallback chains, and provider-agnostic routing layers.
Intelligent document processing
Layout-aware extraction plus LLM reasoning for invoices, contracts, claims, and KYC packets — confidence-scored, human-reviewed at the edges, and posting straight into your systems of record.
Enterprise AI assistants & copilots
Domain copilots grounded in your knowledge bases with citation-backed answers, role-based access control inherited from source systems, and full conversation audit trails.
Evaluation & AI quality engineering
Golden datasets, LLM-as-judge pipelines with human calibration, regression suites in CI, and red-team testing — so every release is quantified before it ships.
AI governance & cost management
PII redaction, audit logging, usage policies, and token-level cost telemetry with per-feature attribution — mapped to your compliance frameworks (SOC 2, HIPAA, GDPR, EU AI Act).
Technology stack
Tools we deploy to production every week
Pragmatic about tools, opinionated about architecture — the platforms below are the ones this practice ships with, chosen per engagement on evidence.
Models & APIs
- OpenAI GPT-5.x
- Anthropic Claude
- Google Gemini
- Llama & Mistral (self-hosted)
- Azure OpenAI
- AWS Bedrock
Orchestration & Retrieval
- LangGraph
- LlamaIndex
- pgvector
- Pinecone
- Elasticsearch
- Temporal
Serving & Evaluation
- vLLM
- Ray Serve
- Langfuse
- Weights & Biases
- Braintrust
- OpenTelemetry
Document AI
- Azure Document Intelligence
- AWS Textract
- Google Document AI
- Tesseract
- Unstructured.io
Implementation process
Five stages. No surprises.
A delivery model refined over 250+ engagements — sequenced so leadership gets visibility and your teams get momentum.
Assess & prioritize
We inventory candidate use cases across your business, score each on ROI, feasibility, and data readiness, and select a first initiative with a measurable business owner and a 90-day path to value.
Baseline & architect
We build a golden evaluation dataset from your real cases, establish accuracy and cost baselines across candidate models, and design the target architecture — including security, data flows, and integration contracts.
Build & evaluate
Senior-led squads ship in two-week sprints, with evaluation scores tracked release-over-release. Prompt changes, retrieval tuning, and model swaps are all regression-tested before merge.
Harden & deploy
Red-team pass, guardrail and PII-redaction verification, load testing, staged rollout behind feature flags, and human-review workflows wired in before real traffic arrives.
Operate & scale
Post-launch we tune costs (caching, routing, model right-sizing), monitor drift, expand to adjacent use cases, and run enablement so your team owns the platform outright.
Use cases
Where enterprises apply it
Contract intelligence
Extract obligations, renewal dates, and non-standard clauses from thousands of legacy contracts; flag deviations from your playbook for legal review.
Claims & case triage
Classify, extract, and route inbound claims or support cases with confidence scoring — straight-through for the routine 80%, prioritized human review for the rest.
Knowledge assistant for operations
A citation-backed copilot over SOPs, product docs, and past tickets that cuts new-hire ramp time and deflects repeat questions from senior staff.
Invoice & AP automation
Line-item extraction, PO matching, and exception routing that takes accounts payable from days of keying to hours of review.
Regulatory document monitoring
Continuous parsing of regulator publications and policy updates, mapped against your internal controls with change alerts to compliance owners.
Customer communication drafting
Grounded draft responses for service teams — tone-controlled, policy-checked, and always presented for human approval before send.
Outcomes
Results clients report to their boards
68%
of document volume straight-through processed without human touch at a global insurer
5.2x
median first-year ROI across our last twenty AI engagements
43%
reduction in per-task inference cost after routing and caching optimization
90 days
typical time from kickoff to first production release
FAQs
Questions leaders ask us
Direct answers on ai solutions — the same ones we give in the first consultation.
Related services
Practices that pair with this one
Ready to put ai solutions to work?
In a 45-minute consultation, our architects map your highest-ROI opportunity, outline a delivery plan, and give you a realistic budget range — no obligation.