Contra · delivery
All delivery lines — filter by category, open details, or book a scoping call.
LLMs, vector stores and pipelines wired into your product — infrastructure that holds up in production.
(1) Audit — map use cases, data and a cost ceiling per request. (2) Architecture — RAG/cache/queue design with latency and failover plans. (3) Integration build — API and pipeline code plus the prompt/context layer. (4) Validation — eval suite, load tests, cost-per-request checks. (5) Ship & monitor — tracing, alerts, iteration on real traffic.
Custom quote
Chatbots grounded in your data — retrieval done right so answers stay true.
(1) Data audit — source inventory, cleaning, chunking strategy. (2) Retrieval design — embedding/index choice, hybrid search + reranking. (3) Build — pipeline and chat orchestration with citations and memory. (4) Eval harness — groundedness and recall sets, hallucination gates. (5) Launch — feedback loop and continuous index updates.
Custom quote
Custom MCP servers that connect Claude and any LLM to your tools, data and APIs — typed, tested, deployed.
(1) Tool surface — choose the right tools to expose with sane naming and granularity. (2) Schema design — typed params/results, auth and transport spec. (3) Server build — MCP SDK implementation with resources and prompts. (4) Agent testing — Claude-in-the-loop runs against edge and error paths. (5) Publish — versioned releases, registry listing, docs.
Custom quote
Hands-off workflows that read, decide and act — your ops on autopilot, with guardrails.
(1) Process map — inventory manual steps, rank by ROI. (2) Flow design — triggers, branches, human-approval points. (3) Build — scenarios with AI steps and clean data mapping. (4) Guardrails — error paths, retries, cost and rate limits. (5) Handover — runbook, alerts, monthly tune-up option.
Custom quote
Orchestrated agents that reason, plan and act together — built to ship.
(1) Decomposition — split the goal into agent roles and handoffs. (2) Orchestration — LangGraph state design, supervisor vs swarm choice. (3) Agent build — tools, memory and structured outputs per agent. (4) Evals & tracing — task-success metrics with full-run traces. (5) Production — deploy, autoscale, cost and drift monitoring.
Custom quote
Drop AI into your existing product — MCP-powered, with the model that fits your use case.
(1) Model fit — benchmark 2–3 models on your task for price/latency. (2) Bridge design — MCP server into your stack with a permissions model. (3) Integration — feature build inside your product with streaming UX. (4) Safety & cost — guardrails, budgets, fallback chains. (5) Launch — usage analytics and prompt iteration.
Custom quote
One agent, your tools, your rules — scoped, built and shipped on AWS.
(1) Scope — a single job-to-be-done with explicit success criteria. (2) Design — tool and memory schema, prompt architecture. (3) Build — agent loop on AWS with tool integrations. (4) Test — task battery runs, failure recovery. (5) Deploy — serverless with logs and weekly iteration.
Custom quote
From spec to store — full application development with the leading cross-platform stack.
(1) Product spec — features, user stories, scope freeze. (2) UX flows — wireframes, design system, clickable prototype. (3) Build sprints — weekly shippable builds with backend and auth. (4) Device QA — real-device matrix, crash and performance pass. (5) Store launch — ASO, listing assets, phased rollout.
Custom quote
Modern web apps with AI built in — streaming UI, server actions and edge-fast deploys on Vercel.
(1) Spec — pages, data model, AI feature definition. (2) Architecture — App Router and RSC plan, auth and DB choice. (3) Build — UI, server actions, AI SDK streaming. (4) Hardening — performance, SEO, accessibility to Lighthouse 95+. (5) Deploy — Vercel with analytics and preview-driven iteration.
Custom quote
Custom WordPress, designed and shipped end to end — fast, editable and built to convert.
(1) Content map — pages, conversion goals, sitemap. (2) Design system — brand, components, mobile-first comps. (3) Theme build — custom blocks and ACF for an editor-friendly CMS. (4) Speed & SEO — Core Web Vitals, schema, meta. (5) Launch — training video and optional care plan.
Custom quote
A senior builder on your project every week — mobile and web shipped in continuous sprints.
(1) Intake — backlog and priorities, week-1 plan. (2) Sprint plan — scoped weekly goals with demo checkpoints. (3) Build — ship every week with async Loom updates. (4) Review — feedback folded in, next sprint scoped. (5) Repeat — velocity tracked; pause or scale anytime.
Custom quote
From wireframe to deployed product — modern stack, clean design, measurable performance.
(1) Spec — user flows and data model agreed. (2) UI system — Tailwind components on a responsive grid. (3) Build — frontend plus Supabase auth and RLS. (4) Quality — tests, Lighthouse 95+, cross-browser pass. (5) Deploy — CI/CD, monitoring, handover docs.
Custom quote
Your idea as a real, testable product — scoped tight, built fast, ready for first users.
(1) Scope cut — core loop only, 2–3 week plan. (2) Flows — clickable prototype for fast validation. (3) Core build — must-have features with analytics events. (4) Beta — TestFlight/internal testing with real users. (5) Launch — store release, metrics review, next bets.
Custom quote
Backends that scale with you — data models, APIs and cloud infrastructure done right.
(1) Domain model — entities, relations, access patterns. (2) API contract — endpoints, types, versioning plan. (3) Build — DB, API, auth, migrations. (4) Hardening — load and security tests, backup/recovery. (5) Deploy — IaC, CI/CD, runbook and docs.
Custom quote
Full applications with AI at the core — chat, vision, voice and automation as product features.
(1) Concept — AI feature-market fit, model selection. (2) Product design — UX around AI moments with a latency budget. (3) Full-stack build — app plus AI backend, streaming and caching. (4) AI QA — evals, abuse cases, cost ceilings. (5) Launch — usage analytics, prompt and model iteration.
Custom quote
Native-feel apps for both stores from one codebase.
(1) Spec — screens, integrations, estimate. (2) Design system — theming, components, motion language. (3) Build — feature sprints with state management and offline support. (4) Device QA — iOS + Android matrix, performance profiling. (5) Release — Fastlane pipelines, store + OTA updates.
Custom quote
A senior AI engineer inside your team — architecture, reviews and shipping, every week.
(1) Onboard — codebase and infra audit, quick-wins list. (2) Roadmap — AI opportunities ranked into a quarterly plan. (3) Build cycles — weekly shipping cadence with pairing and reviews. (4) Quality — eval coverage grows, cost dashboards live. (5) Compound — your team upskilled; capability stays in-house.
$2,500–4,000/mo retainer
Custom models on your data — fine-tuned, evaluated and served at production latency.
(1) Data prep — collection, cleaning, train/eval splits. (2) Baseline — prompted baseline scores, eval set locked first. (3) Fine-tune — LoRA/QLoRA runs with hyperparameter sweeps. (4) Benchmark — against baseline and base model with regression checks. (5) Serve — quantize, deploy, monitor latency and drift.
From $3,000/project
AI-assisted build sprints with Claude Code, Cursor and v0 — your idea live in front of users in days, not months.
(1) Day 0 scope — one core flow chosen, success demo defined. (2) AI scaffold — Claude Code generates the project base. (3) Iterate daily — build–review loops you watch live. (4) Harden — the core path made solid, real auth and data. (5) Demo day — deployed, shareable, next-step roadmap.
$1,500/sprint (1 week)
Install agentic coding in your org — pipelines, skills and guardrails that multiply your existing developers.
(1) Audit — current workflow, tools, friction inventory. (2) Setup — custom skills, MCP servers, repo conventions. (3) Guardrails — CI hooks, review gates, security policy. (4) Training — hands-on team sessions with playbooks. (5) Adoption — usage metrics and 30-day tuning.
$2,000/setup + training
Realtime voice agents that listen, reason and speak — call handling, coaching and support that never sleeps.
(1) Call flows — scripts, intents, escalation rules. (2) Voice stack — LiveKit plus a realtime model, voice selection. (3) Agent logic — tools, knowledge, interruption handling. (4) Latency tuning — sub-second turns validated on real calls. (5) Launch — telephony or in-app, transcripts and analytics.
From $3,000
Apps that see — document scanning, object recognition and multimodal pipelines, cloud or on-device.
(1) Sample data — real-world image set with edge cases listed. (2) Model select — cloud (Gemini) vs on-device (ML Kit) against accuracy targets. (3) Pipeline build — capture → infer → act with fallback paths. (4) Eval — precision/recall on the set, field-condition tests. (5) Ship — on-device optimization, telemetry, retraining loop.
From $2,500
Smart contracts and dApps shipped end to end — audited logic, clean UX, real users.
(1) Contract spec — token/logic design with a threat model. (2) Develop — Solidity with full test coverage and gas optimization. (3) Audit — static analysis, review, testnet battery. (4) dApp front-end — wallet UX, signing, clean onboarding. (5) Mainnet — deploy, verify, monitoring and ops.
From $4,000
Stripe billing, subscriptions, paywalls and payout flows — built reliable, webhook-safe and compliance-aware.
(1) Money map — every flow from charge to payout, edge cases listed. (2) Architecture — Stripe object design with an idempotency plan. (3) Build — billing, paywalls, webhook-safe handlers. (4) Compliance pass — PCI scope, SCA, dunning, refunds. (5) Go-live — reconciliation reports, alerting on money paths.
From $2,000
Red-team your AI before someone else does — injection defense, guardrails and audit trails.
(1) Threat model — attack surface mapped to OWASP LLM Top 10. (2) Red-team — injection and jailbreak battery, data-exfiltration probes. (3) Guardrails — input/output filters, permission boundaries. (4) Harden — fixes with regression evals, audit logging. (5) Report — findings + fixes doc, retest, sign-off.
$1,500/audit
From raw sources to AI-ready knowledge — ingestion, distillation and retrieval that scale.
(1) Source audit — systems, formats, freshness requirements. (2) Pipeline design — schema, flow plan, distillation strategy. (3) Build — ingest and transform, embedding and indexing. (4) Quality — dedup, drift checks, retrieval benchmarks. (5) Serve — APIs and retrieval layer with scaling and monitoring.
From $3,000
AI that moves things — vision-guided control, simulation and edge inference for real machines.
(1) Task spec — motion and sensor needs inside a safety envelope. (2) Simulation — digital twin setup, policy training. (3) Perception + control — vision and planning stack as ROS 2 nodes. (4) Sim-to-real — calibration, field tests, failure modes. (5) Edge deploy — Jetson optimization with remote telemetry.
From $4,000
