AI Agents & Copilots
Conversational AI that actually does things — customer support agents, ops copilots, sales SDRs, internal Q&A. Tool-use, function calling, multi-turn memory.

Custom AI integrations, agents, RAG systems, workflows, and intelligent business tools — built by a team currently operating ORBIX, Kothalipi, NOBBYO, Pannakhata, VoiceBridge, GiftNao, ShadGhor and three more in parallel. We've already made the mistakes; you get the patterns.
Conversational AI that actually does things — customer support agents, ops copilots, sales SDRs, internal Q&A. Tool-use, function calling, multi-turn memory.
Vector search, semantic Q&A, document chat — built on your data, not just public knowledge. Hybrid search, re-ranking, eval-driven retrieval.
Multi-step AI automation, agent chains, async jobs — for content ops, lead enrichment, report generation, and anything that used to take a junior 8 hours.
Fine-tuning, distillation, on-prem deployment, AI strategy advisory. When off-the-shelf isn't enough — or when privacy / cost / latency demand something custom.
Honest call: is this an AI problem, or a regex/SQL/SaaS problem in disguise? Cheaper, faster, less hype.
2-week proof of concept with an eval baseline — accuracy, latency, cost numbers before you commit to production.
Robust, monitored, eval-pipelined. Real users, real failure modes, real cost controls. Weekly demos throughout.
Drift detection, eval pipelines, model swaps, prompt iteration. AI products need ongoing care — like any other product.
10-module Business OS with ⌘K Smart Add, Bangla voice, and AI insights baked into every module. Live in 3 markets.
Real-time voice-to-text for Bangla and other under-served languages. Custom-fine-tuned models on indigenous datasets.
Smart matching between job seekers and employers, in Bangla and English. Tagline: বাংলাদেশের ১ নম্বর AI চাকরির প্ল্যাটফর্ম.
AI-assisted bookkeeping for small businesses. Categorize transactions, generate reports, answer "where did my money go" in Bangla.
Real-time AI voice translation for cross-language conversations. Sub-1-second latency, 12 language pairs in beta.
AI-curated gifting platform for Bangladesh's growing middle class. Answer 4 questions, get a hand-picked shortlist.
AI recipe + meal planning for Bangladeshi home cooks. Bangla-first, budget-aware, ingredient-substitution savvy.
Honestly? Maybe not. Step 1 is always the fitness audit — half the "AI use cases" we hear are better solved with SQL, a regex, or a $20/mo SaaS. We'll tell you on the call, even if it means no project for us.
Depends on the workload. Claude (Anthropic) for most agent / writing / reasoning work. GPT-4o for vision + voice. Open-source (Llama, Mistral, Qwen) when privacy / cost / latency demand on-prem. We benchmark across vendors on your eval set.
Three layers: (1) eval pipelines that catch regression before deploy, (2) RAG / retrieval grounding so the model cites real sources, and (3) UI design that shows confidence + sources, so users can verify. We don't promise zero — we promise managed.
Highly workload-dependent. A simple agent might cost $0.01–0.05 per session. A heavy RAG pipeline maybe $0.10–0.50 per query. We always ship cost guardrails + rate limits + alerts so you never get a surprise bill. Typical SaaS AI feature: 1–4% of revenue.
For most cases, Anthropic / OpenAI enterprise tiers are SOC 2 + zero-retention compliant. For regulated industries (health, gov, finance), we deploy open-source models on-prem or in your VPC — no data leaves your perimeter. Privacy guidance is part of the audit step.
Yes — we're Bangla-first by background. ORBIX, Kothalipi, Pannakhata, ShadGhor all ship in Bangla. Claude + GPT-4 handle Bangla reasonably well; for accent / dialect work we use Kothalipi (our own fine-tuned model).
Every project ships with an eval pipeline — a curated dataset of inputs + expected outputs, run automatically on every deploy. We use LangSmith / Helicone / LangFuse for tracing, and human review for fuzzy outputs. No eval = no deploy.
Architectural choice. We default to model-agnostic abstractions (Vercel AI SDK, LangChain, or our own thin wrapper) so swapping Claude ↔ GPT ↔ Llama is a one-line change. The lock-in usually comes from prompts + eval sets — and those are yours, in your repo.
30-minute discovery call, on Zoom, on me. We'll tell you in 30 minutes whether AI is the right answer — and if so, what the cheapest path to shipping looks like. No deck, no pitch.
I usually reply within an hour, BD daytime. Pick how you want to talk:
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Detailed project chat · 30–60 min