Claude Sonnet 4.6
AnthropicLong-context · tool use · production default
ai development services · live AI software development company,
AI software development company,
built operator-grade.
AI development services and AI development company work for teams shipping real production AI. Generative AI, ML, LLM agents, AI app development, RAG, and vision pipelines, model-agnostic across Claude and GPT, eval-tested, token-optimized. Operator team that uses Claude Code + OpenAI Codex daily. First workflow live in 30 days.
Definition
What is AI software development?
AI software development is the practice of designing, building, and operating production AI systems on top of large language models, vector retrieval, and orchestration frameworks. Unlike classical machine learning which trains a model on labelled data, AI software development composes pre-trained foundation models behind retrieval and tool-use layers. Unlike AI consulting, the deliverable is shipped code with audit logs, not a slide deck. The practice covers LLM apps and RAG, multi-step agents, vision pipelines for document and image work, and Realtime voice. Common stacks blend Claude Sonnet 4.6, GPT-5, and open-source models with pgvector or Pinecone for retrieval, LangGraph for orchestration, and Langfuse for evaluation and observability.
Daily
we use Claude Code + OpenAI Codex internally
30 days
first AI workflow live behind a feature flag
2 models
we ship (Claude + GPT), picked per workflow
Fixed-fee
audit-to-roadmap before any AI build starts
ai development services · what we build Six things an AI software development company
Six things an AI software development company
should be able to ship.
Generative AI development services, AI app development services, machine learning development services, LLM agents, custom AI development, AI product development: covered by one operator team rather than six specialist vendors. Every pattern ships with an eval suite, audit logging, and a token-cost target.
Generative AI development services
Production GenAI applications: copilots, drafting tools, summarizers, classifiers, and structured-extraction pipelines. Claude or GPT picked per workload, eval set rebuilt against your real corpus, monitoring + retry policy shipped with it.
AI app development services
End-to-end AI app development: Flutter and web frontends, FastAPI or Node backends, vector retrieval, auth, billing, telemetry. Operator team that uses Claude Code + OpenAI Codex daily ships your AI app, not a slide deck.
Machine learning development services
Where the LLM isn't the right answer: forecasting, recommendation, anomaly detection, computer vision on edge. We rebuild the eval set, benchmark a baseline (XGBoost, scikit, PyTorch) against an LLM call, and ship whichever wins on your data.
LLM agents in production
Function-calling agents over your real systems: Salesforce, Slack, NetSuite, your repo. LangGraph or hand-rolled, whichever is simpler. Sub-second voice agents on the OpenAI Realtime API for call deflection.
Custom AI development
When off-the-shelf SaaS doesn't fit. RAG over your private corpus (Notion / Drive / Confluence / pgvector / Pinecone), vision pipelines for invoices and claims, multi-vendor routing where compliance demands it. Built around your data model, not ours.
AI product development
Zero-to-one AI product builds: concept validation, eval-first prototyping, design + engineering, and the 8-week production sprint. We co-build with founders who have a thesis and need an operator team, not a consulting deck.
the stack we ship in The stack we ship in,
The stack we ship in,
five layers — pick any to open.
Most AI engineering companies show a logo cloud. We show the layers: frontend, agents, data, infra, eval. Each opens to the tools we name, the production failure modes we've actually hit, and our default unless there's a reason not to. AI native software development isn't a label. It's whether the eval set exists.
- tools we name
- Flutter
- Next.js
- Astro
- React Native
- TailwindCSS
- Server-sent events
- WebSocket streaming
production failure modes- Streaming response that buffers: users abandon at 3 seconds.
- No retry UI when the model fails: silent failure looks like a bug.
- Token-by-token render on top of a slow network = jank that reads as broken.
our default · unless reason not toNext.js or Astro for marketing surfaces and dashboards, Flutter where a single codebase needs to ship mobile + web. Streaming via SSE unless WebSocket is needed for bidirectional audio.
- tools we name
- Claude Sonnet 4.6
- Claude Haiku 4.5
- GPT-5
- GPT-5-mini
- LangGraph
- OpenAI Assistants
- Custom Python
- Tool use
production failure modes- Agent loops on the same tool 12 times before hitting a max-step cutoff.
- Long-context drift: by turn 8 the agent forgets the original instruction.
- Tool-use schema doesn't match what the model emitted, causing silent JSON parse failures.
our default · unless reason not toHand-rolled Python tool loops for anything under ~6 steps; LangGraph when the graph branches. We pick Claude Sonnet for long-context tool runs, Haiku or GPT-5-mini for high-volume narrow tasks. Routing is a deliberate per-call decision, not a default.
- tools we name
- pgvector (Postgres)
- Pinecone
- Weaviate
- OpenSearch
- Cohere Rerank
- BM25 hybrid
- Unstructured.io
- LlamaIndex
production failure modes- Embeddings re-index on every deploy: the vector DB cost balloons quietly.
- Chunking strategy ignores document structure, so answer quality plateaus.
- No reranker — top-k retrieval returns plausible-but-wrong context.
our default · unless reason not topgvector on your existing Postgres for ≤2M chunks (operationally simpler, no extra vendor); Pinecone or Weaviate past that. Hybrid BM25 + dense retrieval + Cohere Rerank by default — the quality lift is bigger than picking a fancier embedding model.
- tools we name
- AWS (Bedrock + Lambda + ECS)
- Azure (Azure OpenAI + Container Apps)
- GCP (Vertex)
- Anthropic API direct
- OpenAI API direct
- Cloudflare Workers
- PrivateLink
- KMS encryption
- SOC 2 / HIPAA BAA
production failure modes- AI vendor outage takes the whole app down with no multi-vendor failover.
- Logs of prompts hit the wrong region, causing an accidental data-residency breach.
- Cold-start latency on serverless adds 800ms to a sub-second voice agent.
our default · unless reason not toAnthropic + OpenAI direct for fastest model access; Azure OpenAI or AWS Bedrock when compliance posture (HIPAA BAA, SOC 2, FedRAMP) requires it. Multi-vendor failover wired in for any workload above monthly run cost. A single vendor is a liability we won't sign off on.
- tools we name
- Langfuse
- Braintrust
- Inspect AI
- Hand-rolled pytest harnesses
- Shadow-mode traffic mirroring
- Golden-set regression
production failure modes- No eval set: "works on my prompt" ships to production and quietly degrades.
- Eval set is the engineer's hand-curated questions and doesn't survive a model swap.
- Drift detected in prod but no rollback path because the previous version wasn't pinned.
our default · unless reason not toEval suite is the first thing we build, before any agent code. Langfuse for prompt + trace observability, Braintrust or a hand-rolled pytest harness for the golden set, shadow-mode mirroring before every cutover. If there's no eval, there's no ship — even from us.
Defaults reflect our current operator playbook (2026). Picked per workflow, not per partner badge — the rationale is in the per-layer detail.
ai consulting and development Consulting, build, or continuous —
Consulting, build, or continuous —
three engagement shapes.
Custom AI development isn't one product; it's three engagement shapes that serve different stages. Most clients arrive at the middle (pilot), some need strategy first, some need ongoing capacity. Same operator team, different cadence. The fit-test is the audit. The strategy-first path lives on our AI consulting page.
Dimension
AI Consulting Strategy · roadmap · audit
You're here
AI Development Pilot Single workflow · 4–8 weeks
Continuous AI Team Embedded squad · monthly cadence
What you walk away with Concrete deliverables, not slides.
AI Consulting Roadmap document + per-workflow model recommendation
AI Development Pilot One AI workflow live in production · eval + runbook
Continuous AI Team 3–5 workflows shipped over the engagement window
Best when Where this engagement model fits.
AI Consulting You're not sure what to build yet
AI Development Pilot You know what to build · need the eng team
Continuous AI Team You have a roadmap · need ship cadence
Commitment What you're signing up for.
AI Consulting 1 week · Fixed-fee · no follow-on obligation
AI Development Pilot 4–8 weeks · fixed-bid fixed · walk-away point
Continuous AI Team Monthly · monthly · cancel any month
Risk to you What happens if it doesn't work.
AI Consulting Low: you keep the roadmap, build with anyone
AI Development Pilot Capped; kill point built into the pilot
Continuous AI Team Cancel-any-month · we hold the trust, not a contract
Average path How most clients actually arrive.
AI Consulting ~30% start here · become pilot afterward
AI Development Pilot ~50% start here · they know what they want
Continuous AI Team ~20% jump straight to embedded · usually post-pilot
Engagement distribution from shipped client work. Your path may differ. The kill point on the pilot is non-negotiable; we'd rather lose phase 2 than ship a workflow that won't move the metric.
how to pick a top ai development company Ten things that separate
Ten things that separate
a real AI dev company from a slide deck.
The "top AI development companies" listicles measure the wrong things: team size, year founded, awards. Buyers should grade on the operating practice. Here's the rubric we'd score ourselves on if we were on the other side of the discovery call.
your vendor scorecard
0/10 keep looking
tap pass / fail on each criterion · saved locally in your browser
-
01 Eval set first
Builds the eval suite before any agent code. Shows you the golden set and the regression test before shipping a feature.
"We'll add evals once it's working." Eval set is the engineer's three hand-curated prompts in a Notion doc.
-
02 Model selection rationale
Picks per workflow with the data. Will tell you why GPT-5-mini won the classifier and Sonnet 4.6 won the long-context summarizer.
Defaults to whichever model the founder posted about most recently on X. Single-vendor stack with no failover.
-
03 Token-cost transparency
Projects per-workflow token cost before the contract. Has a written playbook for routing, caching, and batch APIs.
Quotes a project price but won't tell you what the model bill will look like at steady state. "That depends."
-
04 Operator detail
Names the specific tools their engineers use daily (Claude Code, OpenAI Codex, LangSmith, Langfuse). Has a take on each.
Says "we use industry-leading tooling." Slides full of partner logos without a single named SDK or framework.
-
05 Honest negative space
Will say "don't use us for this" or "that workload is wrong for an LLM." Recommends a non-AI baseline first.
Every workflow is a perfect fit. Every meeting ends with "we can definitely do that." Nothing is out of scope.
-
06 Shipped, not slide-deck
Shows actual production traces, anonymized capability patterns with metrics, a real repo or PR you can read.
Case-study page is stock-logo grids. "Trusted by" companies that turn out to be ex-employee LinkedIn networks.
-
07 Compliance posture
Will deploy on Azure OpenAI / AWS Bedrock with BAA, PrivateLink, KMS. Has a DPIA template. Knows the SOC 2 questionnaire.
"Yes we're SOC 2." Can't produce the report or name the auditor. PII handling pattern is "we'll figure it out."
-
08 Pricing structure
Fixed-fee audit, fixed-price pilot, monthly continuous. Published prices, no hidden tiers. Kill point on every pilot.
Custom-quote-only. Pricing pages that say "contact us." Pilot bills that mysteriously double in week 6.
-
09 Multi-vendor by default
Ships Claude and GPT in the same codebase. Has a routing layer. Treats vendor lock-in as a risk to be engineered around.
Single-vendor partner badge on the homepage. Will tell you whichever model you ask about is "obviously the best."
-
10 Team you can read about
Engineers with public repos, talks, or articles. Open-source contributions you can verify on GitHub.
Generic team page with stock photos. The engineers you'd actually work with are never on the discovery call.
Copy this rubric into your next AI vendor discovery call. If the answer to any criterion pivots to a slide rather than a specific tool name, that's the data point.
llm development services · model picking
What AI development companies ship.
What AI development companies ship.
Four model families, picked per workflow.
LLM development company work that ships only one vendor is rarely about the model. It's about a partner badge. We pick across Claude, GPT, open-weights, and Gemini per workload on the eval data. The four cards on the right are how we frame the trade-off before we look at numbers.
token economics for ai development How we cut an AI development bill
How we cut an AI development bill
without making the model dumber.
Four tactics stacked. Each one independently saves money; together they typically bring effective token cost to 8–15% of the naive baseline, at the same eval-suite quality. The playbook is identical whether you're on Claude, GPT, or a multi-vendor router.
Naive baseline 100% of the bill
What we ship 9% same eval quality
the build · 8 weeks end-to-end How a top AI development company ships
How a top AI development company ships
an 8-week pilot, from audit to cutover.
The shape is the same every time: eval set first, kill point written into the SOW, shadow-mode before cutover, token-optimization pass after. Most pilots ship in 5–8 weeks; the audit upfront is the part that prevents week-6 surprises.
- Weeks 1–2
Audit + eval set
We rebuild the eval suite against your real data, audit the candidate workflows, project per-workflow token cost, and pick the model per workload. You see the data, not our opinion.
Eval set + ranked workflow roadmap - Weeks 3–4
Pilot scope + kill point
We pick the highest-ROI workflow, draft the architecture, agree the success metric, and write down the kill point in the SOW. If the eval doesn't move during the build, we stop. No phase 2.
Signed pilot · success metric · kill pointWalk-away point - Weeks 5–7
Build + shadow-mode
We build the workflow end-to-end against your real systems, deploy behind a feature flag, run shadow mode against your current pipeline (or manual baseline). You see quality + cost on real traffic before cutover.
Production build · shadow metrics report - Week 8 + ongoing
Cutover + token-cost pass
Cutover behind the flag. We run the token-optimization pass: routing, caching, Batch API. Monthly cost-of-ownership and drift report from month 2 onward. Most workflows hit 30–60% cost reduction post-cutover.
Live workflow · monthly $/workflow report
capability patterns AI workflows we've shipped.
AI workflows we've shipped.
Three patterns, three industries.
Three anonymized capability patterns drawn from real engagements across fintech, healthcare, and ecommerce. Named references shared under NDA once we know what you're building.
Claude-powered tier-1 deflection
Problem
Support team drowning in a long tail of "how do I configure X" tickets; tier-1 reps spending most of their time on a small set of repeating questions.
Approach
RAG agent over the product docs + past tickets, Claude Sonnet 4.6 for the synthesis step, Haiku 4.5 for the cheap classification step. Zendesk integration with draft-mode replies (human reviews before send).
Claude Sonnet 4.6Haiku 4.5pgvectorZendeskLangfuse
Outcome
Read the full case study
42% tier-1 ticket deflection (2026-Q1)
GPT-5 vision claims extraction
Problem
Claims adjusters manually extracting fields from scanned forms + accident-scene photos; high error rate on multi-document submissions; backlog growing.
Approach
GPT-5 vision pipeline on Azure OpenAI (PrivateLink, BAA) reads photos + forms and returns structured JSON with confidence per field. Sub-threshold confidence routes to an adjuster with the AI's interpretation attached for review.
GPT-5 visionAzure OpenAIPrivateLinkLangfuse
Outcome
Read the full case study
84% straight-through rate (2026-Q1)
ML + LLM hybrid fraud workflow
Problem
Marketplace listing fraud — fake listings, image-stolen products, copy-pasted descriptions. Pure-ML classifier hit a precision ceiling; pure-LLM was too expensive at listing-creation throughput.
Approach
Hybrid: XGBoost classifier on structured signals (account age, image hash, price delta) decides the easy cases; Claude Haiku 4.5 reviews the gray-band 8%. Disagreement routes to human moderation.
XGBoostClaude Haiku 4.5pgvectorperceptual hashBraintrust
Outcome
Read the full case study
3.1× fraud caught vs ML-only baseline (2026-Q1)
Deeper industry context: fintech AI development, healthcare AI development, and ecommerce AI development — each pillar covers compliance posture, eval shape, and shipped patterns for that vertical.
Further reading from our cluster: what AI software development actually means, 10 generative AI development use cases we've shipped, consulting vs build engagement shapes, how to grade top LLM development companies, and what responsible AI development looks like in production. AI application development services often start at one of these guides.
engagement models Three ways to hire an AI development company.
Three ways to hire an AI development company.
Audit, pilot, or continuous team.
Same engagement shape as our other pillars, consistent across our Claude, OpenAI, and integration work. Most clients begin with the audit to scope, run a 5–8 week pilot on the highest-ROI workflow, then move to monthly for the next 3–5. Outcome-priced engagement — you hire AI developer capacity attached to a workflow with a kill point, not heads attached to a Jira board.
1–2 weeks
AI development audit
Find the AI workflows worth shipping before you commit a budget.
Fixed-fee fixed
- Workflow inventory + ranked ROI shortlist
- Per-workflow model recommendation (Claude / GPT / open-weights)
- Token-cost projection at steady state
- Stack recommendation across the 5 layers
- 90-day implementation roadmap with named workflows
5–8 weeks
AI development pilot
One AI workflow shipped end-to-end with eval data, not a demo.
Fixed-bid fixed price
- Eval set rebuilt against your real data
- Build, integrate, deploy behind a feature flag
- Shadow-mode metrics vs your current baseline
- Token-optimization pass post-cutover
- Walk-away point: if the metric won't move, no phase 2
Monthly
Continuous AI team
Embedded squad shipping the next AI workflow on your roadmap.
monthly per month
- PM + AI engineer + ops analyst, embedded
- Monthly cost-of-ownership + token-spend report
- Drift, eval, and retry-rate monitoring
- Cancel any month, no annual contract
Your repo, your prompts Claude + GPT + open-weights, picked per workflow BAA / DPA available Model-agnostic, openly
frequently asked Questions AI buyers ask most.
Questions AI buyers ask most.
Real answers, including when to walk away.
What is the best AI development company for production workloads?
Best is the wrong frame. The right AI development company is the one that builds the eval suite first, picks models per workflow rather than per partner badge, projects token cost at steady state before you sign, and tells you when an LLM is the wrong answer. Top AI development companies show anonymized capability patterns with real metrics, name the engineers you'll work with, publish how they ship, and have open-source work you can verify on GitHub. We're a small operator team — Dallas + Bengaluru, founded 2017, model-agnostic across Claude and GPT. If you want a 200-person AI consulting firm with a strategy deck, we're the wrong fit. If you want engineers who use Claude Code daily and ship code, we're a fit.
How much does it cost to hire an AI development company?
Three engagement shapes, no surprises in the body of this answer — we hand the dollar figures over in the discovery call so they reflect your workflow shape, not a price-list line. Engagement shapes: a one-to-two week discovery audit (workflow inventory, model recommendation per workflow, token-cost projection, 90-day roadmap), a five-to-eight week pilot (one AI workflow shipped end-to-end with eval, monitoring, runbook), and a continuous AI team (embedded PM + AI engineers shipping integrations monthly with cost-of-ownership reporting). Per-workflow run cost at steady state typically lands in the $200–$1,500/month range depending on volume and model tier — that's the only public dollar figure because it's a technical anchor, not engagement pricing. Most clients self-qualify through the audit conversation.
AI development company vs AI consulting firm vs AI automation agency — what's the difference?
Three different engagement shapes that sometimes get sold under the same name. An AI consulting firm delivers strategy: workload audit, model recommendation, token-cost projection, 90-day roadmap. Document, not code. An AI development company builds: one workflow shipped end-to-end with eval, monitoring, runbook, code-ownership transferred. An AI automation agency tends to focus on workflow orchestration — Zapier-class tooling plus an LLM call, often for ops or marketing use cases. We do all three under one team because the boundaries are squishy in practice — but most engagements are pilot-shaped (build) with a one-week audit upfront and a continuous tail. If you only need strategy, our AI consulting pillar is the entry point.
What does an AI software development company actually do?
Most credible AI software development companies do four things: scope which AI workflows are worth building (audit), build them against your real systems (pilot), run them in production with monitoring + drift detection (continuous), and tell you when AI is the wrong answer (where forecasting, rule-based systems, or a SaaS tool will outperform an LLM call). We do all four. The shape of a good AI development engagement is rarely "build me a chatbot." It's rebuild the eval set, pick the right model per workload, ship one workflow end-to-end with a kill point, and report on cost-of-ownership monthly afterward.
How do I pick the best AI development company for our workload?
Read the vendor rubric above — the 10 criteria are the ones we'd grade ourselves on. The short version: a credible AI development company builds the eval suite first, picks models per workflow rather than per partner badge, will project token cost at steady state before you sign, and will tell you when an LLM is the wrong answer. Top AI development companies show you anonymized capability patterns with real metrics, not stock-logo client grids. They publish pricing. They name the engineers you'll actually work with. They have public open-source work you can verify on GitHub. If a vendor can't answer "what's your eval methodology?" without pivoting to a slide, that's the answer.
What's the difference between AI consulting and AI development services?
AI consulting is strategy: we audit your existing workload (or evaluate a future one), recommend which model fits each use case, project token costs, and give you a 90-day implementation roadmap. We deliver a document, not code. AI consulting and development together is the most common path. Most teams start with a one-week audit (fixed-fee) to scope what's worth building, then move into a development pilot (fixed-bid) for the highest-ROI workflow. Some teams already know what they want shipped and skip the audit. Both paths are fine. The audit is a fit-test, not a gate.
Do you offer generative AI development services or just LLM integration?
Both. Generative AI development services is the umbrella: production GenAI applications (copilots, drafting tools, summarizers), structured-extraction pipelines on images and documents, LLM agents that take actions against real systems, voice agents on the OpenAI Realtime API, and RAG over your private corpus. LLM development services specifically is the deeper engineering of the agent layer: tool-use schemas, multi-step orchestration, prompt caching, eval suites, and drift monitoring. We treat them as one engagement; you don't have to pick.
Can you build an AI app — Flutter, React Native, or web — end-to-end?
Yes. AI app development services is one of our most-shipped patterns. We build your AI app end-to-end: frontend (Next.js, Astro, or Flutter when a single codebase needs to ship mobile + web), backend (FastAPI or Node), retrieval (pgvector or Pinecone), agent layer (Claude or GPT), auth/billing, and telemetry. The AI part is one variable in a normal app build; we don't treat it as a separate workstream that needs its own vendor. An AI app development company that can't ship the actual app is selling consulting under a different name.
How is machine learning development services different from LLM work?
Some workloads aren't a fit for an LLM call. They need classical machine learning. Forecasting (sales, demand, inventory), recommendation systems, anomaly detection, computer vision on edge devices, and high-volume narrow classification all typically belong in XGBoost, scikit-learn, or PyTorch territory rather than "prompt a model." Machine learning development services is where we build those. We often ship hybrid systems: ML for the structured-signal majority, an LLM for the gray-band cases the classifier isn't confident on (see the marketplace fraud capability pattern above). The point is to use the cheaper tool when it works and only escalate to an LLM when the cheaper tool can't.
What does it cost to hire an AI engineer or AI developer through you?
Three engagement tiers, no surprises. A one-week audit is fixed-fee: workflow inventory, model recommendation per workflow, token-cost projection, stack recommendation, 90-day roadmap. A pilot is fixed-bid, 5–8 weeks, covering one AI workflow shipped end-to-end with eval, monitoring, and runbook. A continuous AI team is monthly: embedded PM + AI engineers shipping integrations on your roadmap with monthly cost-of-ownership reporting. Per-workflow run cost at steady state typically lands at $200–$1,500/month depending on volume and which model tier the workflow uses. If you need to hire AI developers as standalone capacity (no scope, just heads), we're not the best fit. We're an outcome-priced operator team, not a staffing shop.
When should we NOT hire an AI development company?
Three honest cases. (1) The workflow doesn't need an LLM. Rule engines, structured ETL, or a SaaS tool will outperform an AI call at a fraction of the run cost. We'll tell you in the audit if that's you. (2) You don't have the data yet. If the eval set can't be built because the underlying business process isn't measured, the AI build is premature. Fix the measurement first. (3) You need an in-house AI org built. A vendor (us included) is the wrong shape; you need a head of AI engineering and a hiring plan, and we'd recommend an executive search firm. The honest answer to "should we hire an AI development company?" is "sometimes." We're glad to be one of those times — but not when we're not.
Ready to ship Book an AI development audit
Book an AI development audit
and see the workflow shortlist.
One week, fixed-fee. We rebuild your eval set against your real data, rank the candidate AI workflows by ROI, project token cost at steady state, recommend the model + stack per workflow, and hand you a 90-day implementation roadmap. No deck, no obligation to build with us afterward.
Fixed-fee · 1–2 weeks Eval set is yours to keep Roadmap works with any vendor
keep exploring Related pages.
Related pages.
Pick where you are.
AI development almost always overlaps with model-specific work, integration, or strategy. These pillars go deeper on each.
AI Chatbot Development
Production conversational AI on Claude Sonnet 4.6 + GPT-5-mini. RAG-grounded, confidence-gated, deployed to web / WhatsApp / Slack. The chat-specific build pillar.
Claude Development
Anthropic Claude integration, Sonnet 4.6 + Haiku 4.5 agents, the sibling model pillar.
OpenAI Development
GPT-5, Realtime API voice agents, OpenAI Codex: the other half of our model stack.
AI Integration Services
Plug Claude or GPT into Salesforce, Slack, NetSuite, and your existing systems.
AI Automation Agency
Production workflow automation in 6–8 weeks. Agents doing the work, not assisting it.
AI Consulting
Strategy and roadmap engagement before the build. Fit-test, not a gate.
AI Agent Development
Multi-step autonomous agents with LangGraph and model-agnostic tool use.
AI Voice Agents
Realtime voice agents on OpenAI Realtime API, Twilio, Vapi, or Deepgram Voice Agent — the voice-channel sibling of the broader AI development engagement.
Updated May 23, 2026 · By Navin Sharma