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AI & Machine Learning

Our AI and machine learning development services take a use case from idea to a system real users depend on. We build copilots and assistants grounded in your own data, agents that complete multi-step tasks, and predictive models for forecasting, scoring, and anomaly detection. Each one ships with evaluation tests, monitoring, and a budget for cost and response time, so it works in front of customers and not only in the demo.

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Most AI projects fail in production, not in the demo

A prototype that answers five hand-picked questions is easy. A system that answers ten thousand real ones, cites its sources, refuses when it should, and costs what you budgeted is engineering work.

We build that system: retrieval grounded in your own data, agents that call your APIs under real business rules, and classical models where a language model is the wrong tool. Evaluation, tracing, and cost controls go in from the first sprint, not after the first incident.

2 wks

Discovery to a scoped, ranked AI roadmap

6–10 wks

Prototype to a production-ready first release

Eval-first

Every model change tested against your real questions first

Your cloud

Runs in your accounts, your data stays yours

What's included

Generative AI, LLM agents, and machine learning models that hold up in production, with evaluation, guardrails, and cost control from day one.

Generative AI products, copilots, and chat assistants
LLM agents, tool calling, and workflow automation
RAG search over your private documents and data
Model fine-tuning and private, open-weight model hosting
Predictive ML: forecasting, scoring, and anomaly detection
MLOps, evaluation, and model monitoring

Four things separate an AI feature from an AI product

Every engagement is assembled from these four layers. Skip one and you get the demo that never quite ships.

01

Ground it in your data

Hybrid retrieval across documents, tickets, and warehouse tables, with chunking, reranking, and citations so every answer traces back to a source a human can open.

02

Let it reason and act

Agents that call your APIs, respect your business rules, and hand off to a person when confidence drops, rather than chat that only sounds convincing.

03

Prove it works

Golden datasets, regression suites, and model-graded scoring wired into CI, so a prompt tweak or model upgrade can never silently degrade quality.

04

Run it economically

Model routing, caching, quantization, and streaming tuned against a token, latency, and GPU budget you approved before launch.

AI and machine learning development services for real workflows

We start from the workflow that costs you money or time, then choose the model class that fits, from a frontier LLM to a gradient-boosted tree.

Enterprise copilots & assistants

Assistants that work over your private knowledge with permissions intact, so support, sales, and internal teams stop hunting through wikis and PDFs.

  • Grounded answers with source citations
  • Role-aware access control
  • Deflection and satisfaction tracking

Agentic workflow automation

Multi-step agents that read a request, gather context, call your systems, and complete the task, with approvals wherever the stakes justify a human.

  • Tool and API orchestration
  • Human-in-the-loop approvals
  • Deterministic fallback paths

Document & multimodal intelligence

Contracts, claims, invoices, images, and call recordings turned into structured, validated records your existing systems can consume.

  • Extraction with confidence scoring
  • Vision-language and OCR pipelines
  • Speech to structured data

Predictive & decision models

Forecasting, scoring, and anomaly detection where accuracy and explainability matter more than fluency, deployed behind clean APIs.

  • Demand, capacity and revenue forecasting
  • Churn, risk and pricing models
  • Anomaly and fraud detection

Model platform & MLOps

The unglamorous layer that keeps AI alive: pipelines, registries, evaluation gates, and dashboards for quality, drift, and spend.

  • Feature and vector store design
  • Model registry with CI/CD gates
  • Drift, quality and cost dashboards

Private & open-weight deployment

For regulated data or high volume, self-hosted models tuned on your examples and served inside your network with no data leaving it.

  • Self-hosted open-weight models
  • Fine-tuning and distillation
  • VPC-only inference, no data egress

Our AI development process, from use-case triage to a system you run

A typical first engagement runs eight to twelve weeks. Each stage ends with something reviewable, so you can stop or expand on evidence.

Stage 01

1–2 weeks

Opportunity mapping

We audit your data, workflows, and constraints, then rank candidate use cases by value, feasibility, and risk, instead of starting from whichever model is trending.

  • Use-case scorecard
  • Data readiness review
  • Target architecture sketch
Stage 02

2–3 weeks

Grounded prototype

A working slice on your real data with retrieval, prompts, and a first evaluation set, so stakeholders judge quality on evidence rather than a scripted demo.

  • Clickable prototype
  • Golden evaluation dataset
  • Cost and latency baseline
Stage 03

3–5 weeks

Hardening & guardrails

Permissions, PII handling, refusal behavior, fallbacks, and regression suites running in CI, plus load testing on the paths that actually carry traffic.

  • Guardrail and safety layer
  • Regression suite in CI
  • Tracing and observability
Stage 04

Ongoing

Launch & improve

Staged rollout with monitoring on quality, cost, and drift, and a change process your engineers can run, including prompt updates and retraining.

  • Staged rollout plan
  • Quality and spend dashboards
  • Runbooks and team enablement

What production-grade AI delivers for your business

Answers users trust, with citations and predictable failure behavior

Hours of manual review, triage, and data entry reclaimed

Token, GPU, and latency budgets that hold as usage grows

An in-house team confident enough to ship AI changes without us

What we build it with

The tools we reach for on AI & ML work, picked for the problem in front of us and for the team who inherits the code.

01

Modeling

Where the training and fine-tuning happens

  • Python
  • PyTorch
  • Hugging Face
  • scikit-learn
02

LLM and agents

Orchestration across models and tools

  • LangGraph
  • LangChain
  • OpenAI
  • Anthropic
  • Ollama
03

Retrieval and serving

Grounding answers, then serving them fast

  • FastAPI
  • vLLM
  • Qdrant
  • PostgreSQL
  • Ray
04

Evaluation and ops

Proof it still works after the next model swap

  • MLflow
  • Weights & Biases
  • Docker
  • AWS

AI development questions we get in the first call

  • It depends on data sensitivity, cost at your volume, and how much control you need over behavior. We usually validate value fast on a hosted frontier model, then benchmark open-weight options once traffic patterns are clear. Mature systems often route: a small model handles easy cases, a large one handles the rest.

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Let's build something that lasts

Tell us about your project and we'll get back to you within 12 hours with next steps.

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